uiuc cs senior thesis

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Graduate Dissertations and Theses at Illinois

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Course Catalog

Cs - computer science.

CS Class Schedule

CS 100   Computer Science Orientation   credit: 1 Hour.

Introduction to Computer Science as a field and career for incoming first year and external transfer students in the computer science majors. Overview of the field and specific examples of problem areas and methods of solution.

CS 101   Intro Computing: Engrg & Sci   credit: 3 Hours.

Fundamental principles, concepts, and methods of computing, with emphasis on applications in the physical sciences and engineering. Basic problem solving and programming techniques; fundamental algorithms and data structures; use of computers in solving engineering and scientific problems. Intended for engineering and science majors. Prerequisite: One of MATH 220 or MATH 221 or MATH 231 or MATH 241 . This course satisfies the General Education Criteria for: Quantitative Reasoning II

CS 102   Little Bits to Big Ideas   credit: 4 Hours.

Same as INFO 102 . See INFO 102 .

CS 105   Intro Computing: Non-Tech   credit: 3 Hours.

Computing as an essential tool of academic and professional activities. Functions and interrelationships of computer system components: hardware, systems and applications software, and networks. Widely used application packages such as spreadsheets and databases. Concepts and practice of programming for the solution of simple problems in different application areas. Intended for non-science and non-engineering majors. Prerequisite: MATH 112 . This course satisfies the General Education Criteria for: Quantitative Reasoning I

CS 107   Data Science Discovery   credit: 4 Hours.

Same as IS 107 and STAT 107 . See STAT 107 . This course satisfies the General Education Criteria for: Quantitative Reasoning I

CS 124   Introduction to Computer Science I   credit: 3 Hours.

Basic concepts in computing and fundamental techniques for solving computational problems. Intended as a first course for computer science majors and others with a deep interest in computing. Credit is not given for both CS 124 and CS 125 . Prerequisite: Three years of high school mathematics or MATH 112 . This course satisfies the General Education Criteria for: Quantitative Reasoning I

CS 125   Introduction to Computer Science   credit: 4 Hours.

Basic concepts in computing and fundamental techniques for solving computational problems. Intended as a first course for computer science majors and others with a deep interest in computing. Credit is not given for both CS 125 and CS 124 . Prerequisite: Three years of high school mathematics or MATH 112 . This course satisfies the General Education Criteria for: Quantitative Reasoning I

CS 126   Software Design Studio   credit: 3 Hours.

Fundamental principles and techniques of software development. Design, documentation, testing, and debugging software, with a significant emphasis on code review. Credit is not given for both CS 242 and CS 126 . Prerequisite: CS 125 . For majors only.

CS 128   Introduction to Computer Science II   credit: 3 Hours.

Continuation of CS 124 . More advanced concepts in computing and techniques and approaches for solving computational problems. Prerequisite: CS 124 or CS 125 . This course satisfies the General Education Criteria for: Quantitative Reasoning II

CS 173   Discrete Structures   credit: 3 Hours.

Discrete mathematical structures frequently encountered in the study of Computer Science. Sets, propositions, Boolean algebra, induction, recursion, relations, functions, and graphs. Credit is not given for both CS 173 and MATH 213 . Prerequisite: One of CS 124 , CS 125 , ECE 220 ; one of MATH 220 , MATH 221 .

CS 196   First-Year Student Honors   credit: 1 Hour.

Offered for honors credit in conjunction with other 100-level computer science courses taken concurrently. A special examination may be required for admission to this course. May be repeated if topics vary, for a maximum of 2 hours in the same semester and a maximum of 3 hours total. Prerequisite: Concurrent registration in another 100-level computer science course (see Schedule).

CS 199   Undergraduate Open Seminar in Computer Science   credit: 0 to 5 Hours.

Topics vary. Approved for Letter and S/U grading. May be repeated.

CS 210   Ethical & Professional Issues   credit: 2 Hours.

Ethics for the computing profession. Ethical decision-making; licensing; intellectual property, freedom of information, and privacy. Credit is not given for both CS 210 and either CS 211 or ECE 316 . Prerequisite: CS 225 . Junior standing required.

CS 211   Ethical and Professional Conduct   credit: 3 Hours.

Navigating the complex ethical and professional landscape of the computing professional: privacy, intellectual property, cybersecurity, and freedom of speech. Hands-on exercises, assignments, and discussions in which students analyze current events from perspectives in both philosophical and professional ethics. Writing professionally and technically in several writing assignments requiring peer review, workshops, and multiple rounds of editing and revising. Credit is not given for both CS 211 and CS 210 or ECE 316 . Prerequisite: CS 225 . This course satisfies the General Education Criteria for: Advanced Composition

CS 222   Software Design Lab   credit: 1 Hour.

Design and implementation of novel software solutions. Problem identification and definition; idea generation and evaluation; and software implementation, testing, and deployment. Emphasizes software development best practices—including framework selection, code review, documentation, appropriate library usage, project management, continuous integration and testing, and teamwork. Prerequisite: CS 128 ; credit or concurrent registration in CS 225 . Restricted to majors in Computer Science undergraduate curricula only.

CS 225   Data Structures   credit: 4 Hours.

Data abstractions: elementary data structures (lists, stacks, queues, and trees) and their implementation using an object-oriented programming language. Solutions to a variety of computational problems such as search on graphs and trees. Elementary analysis of algorithms. Credit is not given for CS 277 if credit for CS 225 has been earned. Prerequisite: CS 126 or CS 128 or ECE 220 ; One of CS 173 , CS 413 , MATH 213 , MATH 347 , MATH 412 , or MATH 413 . This course satisfies the General Education Criteria for: Quantitative Reasoning II

CS 233   Computer Architecture   credit: 4 Hours.

Fundamentals of computer architecture: digital logic design, working up from the logic gate level to understand the function of a simple computer; machine-level programming to understand implementation of high-level languages; performance models of modern computer architectures to enable performance optimization of software; hardware primitives for parallelism and security. Prerequisite: CS 125 or CS 128 ; CS 173 or MATH 213 ; credit or concurrent enrollment in CS 225 .

CS 242   Programming Studio   credit: 3 Hours.

Intensive programming lab intended to strengthen skills in programming. Prerequisite: CS 241.

CS 265   Innovation Illinois: From Accessible Design to Supercomputing Cultures   credit: 3 Hours.

Same as IS 265 and MACS 265 . See MACS 265 .

CS 266   Community Innovation   credit: 3 Hours.

Same as IS 266 and MACS 266 . See IS 266 . This course satisfies the General Education Criteria for: Social & Beh Sci - Soc Sci Cultural Studies - Western

CS 277   Algorithms and Data Structures for Data Science   credit: 4 Hours.

Introduction to elementary concepts in algorithms and classical data structures with a focus on their applications in Data Science. Topics include algorithm analysis (ex: Big-O notation), elementary data structures (ex: lists, stacks, queues, trees, and graphs), basics of discrete algorithm design principles (ex: greedy, divide and conquer, dynamic programming), and discussion of discrete and continuous optimization. Credit is not given for CS 277 if credit for CS 225 is earned. Prerequisite: STAT 207 ; one of MATH 220 , MATH 221 , MATH 234 . CS 277 cannot be taken concurrently with CS 225 .

CS 296   Honors Course   credit: 1 Hour.

Group projects for honors credit in computer science. Sections of this course are offered in conjunction with other 200-level computer science courses taken concurrently. A special examination may be required for admission to this course. May be repeated. Prerequisite: Concurrent registration in another 200-level computer science course (see Schedule).

CS 307   Modeling and Learning in Data Science   credit: 4 Hours.

Introduction to the use of classical approaches in data modeling and machine learning in the context of solving data-centric problems. A broad coverage of fundamental models is presented, including linear models, unsupervised learning, supervised learning, and deep learning. A significant emphasis is placed on the application of the models in Python and the interpretability of the results. Prerequisite: STAT 207 ; one of MATH 225 , MATH 227 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 .

CS 340   Introduction to Computer Systems   credit: 3 Hours.

Basics of computer systems. Number representations, assembly/machine language, abstract models of processors (fetch/execute, memory hierarchy), processes/process control, simple memory management, file I/O and directories, network programming, usage of cloud services. Credit is not given for both CS 340 and CS 240. Prerequisite: CS 225 .

CS 341   System Programming   credit: 4 Hours.

Basics of system programming, including POSIX processes, process control, inter-process communication, synchronization, signals, simple memory management, file I/O and directories, shell programming, socket network programming, RPC programming in distributed systems, basic security mechanisms, and standard tools for systems programming such as debugging tools. Credit is not given for both CS 341 and either CS 241 or ECE 391 . Prerequisite: CS 225 and CS 233 .

CS 357   Numerical Methods I   credit: 3 Hours.

Fundamentals of numerical methods for students in science and engineering; floating-point computation, systems of linear equations, approximation of functions and integrals, the single nonlinear equation, and the numerical solution of ordinary differential equations; various applications in science and engineering; programming exercises and use of high quality mathematical library routines. Same as MATH 357 . Credit is not given towards graduation for CS 357 if credit for CS 450 has been earned. (Counts for advanced hours in LAS). Prerequisite: One of CS 101 , CS 105 , CS 124 , CS 125 or ECE 220 ; MATH 241 ; one of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 or BIOE 210 .

CS 361   Probability & Statistics for Computer Science   credit: 3 Hours.

Introduction to probability theory and statistics with applications to computer science. Topics include: visualizing datasets, summarizing data, basic descriptive statistics, conditional probability, independence, Bayes theorem, random variables, joint and conditional distributions, expectation, variance and covariance, central limit theorem. Markov inequality, Chebyshev inequality, law of large numbers, Markov chains, simulation, the PageRank algorithm, populations and sampling, sample mean, standard error, maximum likelihood estimation, Bayes estimation, hypothesis testing, confidence intervals, linear regression, principal component analysis, classification, and decision trees. Same as STAT 361 . Credit is not given for both CS 361 and ECE 313 . Prerequisite: MATH 220 or MATH 221 ; credit or concurrent registration in one of MATH 225 , MATH 257 , MATH 415 , MATH 416 or ASRM 406 . For majors only.

CS 374   Introduction to Algorithms & Models of Computation   credit: 4 Hours.

Analysis of algorithms, major paradigms of algorithm design including recursive algorithms, divide-and-conquer algorithms, dynamic programming, greedy algorithms, and graph algorithms. Formal models of computation including finite automata and Turing machines. Limitations of computation arising from fundamental notions of algorithm and from complexity-theoretic constraints. Reductions, undecidability and NP-completeness. Same as ECE 374 . Prerequisite: One of CS 173 , MATH 213 ; CS 225 .

CS 397   Individual Study   credit: 1 to 3 Hours.

May be repeated. Prerequisite: Consent of instructor.

CS 398   Special Topics   credit: 1 to 4 Hours.

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. May be repeated in the same or separate terms if topics vary.

CS 400   Accelerated Fundamentals of Computing I   credit: 3 Hours.

The first class in a sequence of two classes that introduces students to the basic concepts in computing with an emphasis on the fundamental techniques for solving computational problems. Topics include: core programming concepts (variables, data types, conditional expressions, loops, functions), basic data structures, searching and sorting algorithms, and data exploration and visualization. No prior programming experience is required. No undergraduate credit. 3 graduate hours. Prerequisite: Current enrollment in the Illinois Computing Accelerator for Non-specialists (iCAN) program or consent of instructor. Restricted to post-baccalaureate students with a non-computing background.

CS 401   Accelerated Fundamentals of Algorithms I   credit: 3 Hours.

The first class in a sequence of two classes that introduces students to the theoretical foundations of computer science. Topics include counting, sets, functions, decision trees, recursion, binary numbers, basic graph theory, depth first search and breadth first search, algorithms for computing shortest paths, data structures like stacks and queues, big O notation and asymptotic analysis, complexity classes like P and NP, and reductions. No undergraduate credit. 3 graduate hours. Prerequisite: Current enrollment in the Illinois Computing Accelerator for Non-specialists (iCAN) program or consent of instructor. Restricted to post- baccalaureate students with a non-computing background.

CS 402   Accelerated Fundamentals of Computing II   credit: 3 Hours.

Provides an accelerated introduction to class-based program design and the design of abstractions that support the design of reusable software and libraries. It reviews typical object-oriented concepts such as information hiding, encapsulation, polymorphism, inheritance, composition, and common design patterns. The course will also covers exception handling, database programming, and concurrent programming. No undergraduate credit. 3 graduate hours. Prerequisite: CS 400 or consent of instructor. Current enrollment in the Illinois Computing Accelerator for Non-specialists (iCAN) program or consent of instructor. Restricted to post-baccalaureate students with a non-computing background.

CS 403   Accelerated Fundamentals of Algorithms II   credit: 3 Hours.

The second class in a sequence of two classes that introduces students to the theoretical foundations of computer science. Topics include major paradigms of algorithm design divide and conquer, greedy, recursive, and dynamic programming; solving recurrences and analysis of divide and conquer algorithms; graph algorithms; formal models of computations like finite state automata and Turing machines; reductions. No undergraduate credit. 3 graduate hours. Prerequisite: CS 401 or consent of instructor. Current enrollment in the Illinois Computing Accelerator for Non-specialists (iCAN) program or consent of instructor. Restricted to post-baccalaureate students with a non-computing background.

CS 407   Cryptography   credit: 3 or 4 Hours.

Same as ECE 407 . See ECE 407 .

CS 409   The Art of Web Programming   credit: 3 or 4 Hours.

Client- and server-side technologies that enable modern Web applications. Topics include the building blocks of the Web (browsers, HTML, CSS, JavaScript) and data exchange (HTTP, AJAX, JSON, REST). Assignments expose students to full-stack web development, and JavaScript frameworks that scaffold MVC architectures and event-driven, asynchronous programming. The course culminates in a final project, where students work in teams to design and develop an original, database-backed web application. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 .

CS 410   Text Information Systems   credit: 3 or 4 Hours.

Theory, design, and implementation of text-based information systems. Text analysis, retrieval models (e.g., Boolean, vector space, probabilistic), text categorization, text filtering, clustering, retrieval system design and implementation, and applications to web information management. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 .

CS 411   Database Systems   credit: 3 or 4 Hours.

Examination of the logical organization of databases: the entity-relationship model; the hierarchical, network, and relational data models and their languages. Functional dependencies and normal forms. Design, implementation, and optimization of query languages; security and integrity; concurrency control, and distributed database systems. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 .

CS 412   Introduction to Data Mining   credit: 3 or 4 Hours.

Concepts, techniques, and systems of data warehousing and data mining. Design and implementation of data warehouse and on-line analytical processing (OLAP) systems; data mining concepts, methods, systems, implementations, and applications. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 .

CS 413   Intro to Combinatorics   credit: 3 or 4 Hours.

Same as MATH 413 . See MATH 413 .

CS 414   Multimedia Systems   credit: 3 or 4 Hours.

Organization and structure of modern multimedia systems; audio and video encoding; quality of service concepts; scheduling algorithms for multimedia within OS and networks multimedia protocols over high-speed networks; synchronization schemes, user-interface design; multimedia teleservices. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: One of CS 240, CS 241, CS 340 , CS 341 or ECE 391 .

CS 415   Game Development   credit: 3 or 4 Hours.

A team and project-based course on the technical aspects of video game development and game engine internals: geometric modeling, game physics and AI, shader programming, real-time physically based rendering, and software engineering practices within the game industry. The central focus of the course is the development of a game by teams of 3 to 5 students. The course strongly emphasizes code development using a modern game engine. Students will gain skills necessary to develop games and to develop game engines. 3 undergraduate hours. 4 graduate hours. Prerequisite: CS 225 .

CS 416   Data Visualization   credit: 3 or 4 Hours.

Elements of databases, computer graphics and perceptual psychology combined to utilize the human visual system to improve user understanding of large datasets. Topics include appropriate and effective selection and construction of charts, organization of dashboards, and design for user engagement and interaction. 3 undergraduate hours. 4 graduate hours. Prerequisite: CS 225 .

CS 417   Virtual Reality   credit: 3 or 4 Hours.

Provides both a deep understanding of the fundamentals of virtual reality (VR) and practical experience implementing VR systems. Topics covered include visual and audio display technology, tracking, human perception and psychophysics, building user interfaces for VR, and analyzing VR experiences. 3 undergraduate hours. 4 graduate hours. Prerequisite: CS 225 .

CS 418   Interactive Computer Graphics   credit: 3 or 4 Hours.

Basic mathematical tools and computational techniques for modeling, rendering, and animating 3-D scenes. Same as CSE 427 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 ; One of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 , or BIOE 210 ; MATH 241 .

CS 419   Production Computer Graphics   credit: 3 or 4 Hours.

Advanced methods for representing, displaying, and rendering two-, three-, and four-dimensional scenes. General algebraic curves and surfaces, splines, Gaussian and bump-function representation, fractals, particle systems, constructive solid geometry methods, lighting models, radiosity, advanced ray-tracing methods, surface texturing animation techniques, data visualization methods. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 418 or CSE 427 .

CS 420   Parallel Progrmg: Sci & Engrg   credit: 3 or 4 Hours.

Fundamental issues in design and development of parallel programs for various types of parallel computers. Various programming models according to both machine type and application area. Cost models, debugging, and performance evaluation of parallel programs with actual application examples. Same as CSE 402 and ECE 492 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 .

CS 421   Programming Languages & Compilers   credit: 3 or 4 Hours.

Structure of programming languages and their implementation. Basic language design principles; abstract data types; functional languages; type systems; object-oriented languages. Basics of lexing, parsing, syntax-directed translation, semantic analysis, and code generation. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: One of CS 233 , CS 240, CS 340 or ECE 391 ; CS 374 or ECE 374 ; one of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 , or BIOE 210 .

CS 422   Programming Language Design   credit: 3 or 4 Hours.

Exploration of major language design paradigms using imperative and functional programming as unifying themes. Tools include both practical language processor construction and theoretical models. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 421 .

CS 423   Operating Systems Design   credit: 3 or 4 Hours.

Organization and structure of modern operating systems and concurrent programming concepts. Deadlock, virtual memory, processor scheduling, and disk systems. Performance, security, and protection. Same as CSE 423 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: One of CS 241, CS 341 , or ECE 391 .

CS 424   Real-Time Systems   credit: 3 or 4 Hours.

Supervisory control aspects of Cyber Physical Systems (CPS): fundamentals of reliability analysis, real-time scheduling, simple feedback control, software fault tolerance architecture, wireless networking and energy saving, principles of safety critical system engineering. Student groups design and demonstrate supervisory control architecture for a robot. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: One of CS 241, CS 341 or ECE 391 .

CS 425   Distributed Systems   credit: 3 or 4 Hours.

Protocols, specification techniques, global states and their determination, reliable broadcast, transactions and commitment, security, and real-time systems. Same as ECE 428 . 3 or 4 undergraduate hours. 3 or 4 graduate hours. Prerequisite: Credit or concurrent enrollment in one of CS 240, CS 241, CS 340 , CS 341 or ECE 391 .

CS 426   Compiler Construction   credit: 3 or 4 Hours.

Compiler structure, syntax analysis, syntax-directed translation, automatically constructed recognizers, semantic analysis, code generation, intermediate language, optimization techniques. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: Credit or concurrent enrollment in CS 421 .

CS 427   Software Engineering I   credit: 3 or 4 Hours.

Software process, analysis and design. Software development paradigms, system engineering, function-based analysis and design, and object-oriented analysis and design. Course will use team-projects for hands-on exercises. Same as CSE 426 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: One of CS 222 , CS 240, CS 241, CS 340 , CS 341 or ECE 391 .

CS 428   Software Engineering II   credit: 3 or 4 Hours.

Continuation of CS 427 . Software development, management, and maintenance. Project and configuration management, collaborative development models, software quality assurance, interoperability domain engineering and software reuse, and software re-engineering. Same as CSE 429 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 427 or CSE 426 .

CS 429   Software Engineering II, ACP   credit: 3 Hours.

Continuation of CS 427 . Identical to CS 428 except for the additional writing component. See CS 428 . 3 undergraduate hours. 3 graduate hours. Prerequisite: CS 427 or CSE 426 . This course satisfies the General Education Criteria for: Advanced Composition

CS 431   Embedded Systems   credit: 3 or 4 Hours.

Students will learn embedded system architecture, Real-Time Operating Systems, and real-time software design and development techniques, including 1) real-time software designs, practical signal processing, and control techniques in embedded applications, including the safe use of machine learning, and 2) the schedulability analysis to ensure deadlines of tasks are met. In the lab, students will practice what they learn in the class so that their software can drive a two-wheeled robot car along a course and avoid obstacles autonomously. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: One of CS 241, CS 341 or ECE 391 .

CS 433   Computer System Organization   credit: 3 or 4 Hours.

Computer hardware design and analysis and interface with software. Advanced processor design, including superscalar, out-of-order issue, branch prediction, and speculation. Memory hierarchy design, including advanced cache optimizations, main memory, and virtual memory. Principles of multiprocessor design, including shared-memory, cache coherence, synchronization, and consistency. Other advanced topics depending on time; e.g., GPUs and accelerators, warehouse computers and data centers, security. Same as CSE 422 . 3 undergraduate hours. 4 graduate hours. Prerequisite: CS 233 .

CS 434   Real World Algorithms for IoT and Data Science   credit: 3 or 4 Hours.

Same as ECE 434 . See ECE 434 .

CS 435   Cloud Networking   credit: 3 or 4 Hours.

Provides a foundational view of computer networks with a focus on enabling modern large-scale cloud computing. The first part of this course studies the principles upon which the Internet and other networks are built, and how those principles translate into deployed protocols. The second part covers how those principles are applied in network infrastructure for modern clouds, enabling deployment of virtual networks on shared infrastructure, efficient transfer of big data and low latency communication, and federation of applications across countries and continents. 3 undergraduate hours. 4 graduate hours. Credit is not given for both CS 435 and CS 438 / ECE 438 . Prerequisite: One of CS 240, CS 241, CS 340 , CS 341 or ECE 391 .

CS 436   Computer Networking Laboratory   credit: 3 or 4 Hours.

Design, application, analysis, and deployment of communication protocols and system software behind modern cloud/compute/network infrastructures. Students learn the internals of modern system infrastructures, including operating system networking kernels, cloud application service code, and firewall and router configuration. Students will gain experience with widely-used and production-grade code and systems, such as Cisco IOS, the Linux networking stack, and Amazon Web Services. This class links theory with practice to prepare students to confidently carry out tasks they will commonly encounter in industry, such as building an enterprise network, deploying a large-scale cloud service, or implementing a new network protocol. Same as ECE 435 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: One of CS 240, CS 241, CS 340 , CS 341 or ECE 391 .

CS 437   Topics in Internet of Things   credit: 3 or 4 Hours.

Topics of networked embedded computing technology, known as the Internet of Things, in application, distributed, human-centric, or social contexts. The tight coupling between people, networking protocols, computing elements, and physical things in IoT systems presents unique challenges for data collection, processing in constrained computation and communication environments. Introduces general principles of IoT systems and protocol, offers broad foundations for IoT services, and allows specialization to pursue an in-depth understanding of selected IoT aspects or subtopics. 3 undergraduate hours. 4 graduate hours. May be repeated if topics vary. Credit towards a degree from multiple offerings of this course is not given if those offerings have significant overlap, as determined by the CS department. Prerequisite: CS 241 or CS 341 . Additional prerequisites may be specified each term. See section information.

CS 438   Communication Networks   credit: 3 or 4 Hours.

Layered architectures and the OSI Reference Model; design issues and protocols in the transport, network, and data link layers; architectures and control algorithms of local-area, point-to-point, and satellite networks; standards in networks access protocols; models of network interconnection; overview of networking and communication software. Same as ECE 438 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: One of CS 241, CS 341 or ECE 391 ; strongly recommend one of CS 361 , STAT 361 , ECE 313 , MATH 362 , MATH 461 , MATH 463 or STAT 400 .

CS 439   Wireless Networks   credit: 3 or 4 Hours.

Same as ECE 439 . See ECE 439 .

CS 440   Artificial Intelligence   credit: 3 or 4 Hours.

Major topics in and directions of research in artificial intelligence: basic problem solving techniques, knowledge representation and computer inference, machine learning, natural language understanding, computer vision, robotics, and societal impacts. Same as ECE 448 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 ; one of CS 361 , STAT 361 , ECE 313 , MATH 362 , MATH 461 , MATH 463 , STAT 400 or BIOE 310 .

CS 441   Applied Machine Learning   credit: 3 or 4 Hours.

Techniques of machine learning to various signal problems: regression, including linear regression, multiple regression, regression forest and nearest neighbors regression; classification with various methods, including logistic regression, support vector machines, nearest neighbors, simple boosting and decision forests; clustering with various methods, including basic agglomerative clustering and k-means; resampling methods, including cross-validation and the bootstrap; model selection methods, including AIC, stepwise selection and the lasso; hidden Markov models; model estimation in the presence of missing variables; and neural networks, including deep networks. The course will focus on tool-oriented and problem-oriented exposition. Application areas include computer vision, natural language, interpreting accelerometer data, and understanding audio data. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 and CS 361 .

CS 442   Trustworthy Machine Learning   credit: 3 or 4 Hours.

Prepares students to understand the security and privacy problems in machine learning and educates students to propose different attack strategies to identify the vulnerabilities of a range of learning algorithms and understand different defense approaches towards trustworthy machine learning systems. Students will explore topics including basic machine learning foundations (e.g., linear regression and PCA), adversarial attacks against different learning algorithms, differential privacy, data valuation, and different categories of defenses. The lessons are reinforced via a series of topic-driven lectures, coding assignments, related paper readings, exams and in-class discussions. Students will learn to analyze current interactions between attackers and defenders on machine learning and therefore develop an understanding of the principles on trustworthy machine learning which is an emerging and important topic. Students will be required to finish three related homework projects, including 1) developing a machine learning classifier, 2) designing adversarial attacks against the built classifier, and 3) developing defenses to improve the robustness of the trained classifier against designed attacks. Students registered for 4 credit hours will also finish a final project based on the class topics, demonstrating their ability to propose related new algorithms based on the class subjects. 3 undergraduate hours. 4 graduate hours. Prerequisite: CS 225 ; one of CS 440 , ECE 448 , CS 441 , CS 446 or ECE 449 ; one of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 or BIOE 210 .

CS 443   Reinforcement Learning   credit: 3 or 4 Hours.

Fundamental concepts and basic algorithms in Reinforcement Learning (RL) - a machine learning paradigm for sequential decision-making. The goal of this course is to enable students to (1) understand the mathematical framework of RL, (2) tell what problems can be solved with RL, and how to cast these problems into the RL formulation, (3) understand why and how RL algorithms are designed to work, and (4) know how to experimentally and mathematically evaluate the effectiveness of an RL algorithm. There will be both programming and written assignments. 3 undergraduate hours. 4 graduate hours. Prerequisite: CS 225 ; MATH 241 ; one of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 or BIOE 210 ; one of CS 361 , STAT 361 , ECE 313 , MATH 362 , MATH 461 , MATH 463 or STAT 400 .

CS 444   Deep Learning for Computer Vision   credit: 3 or 4 Hours.

Provides an elementary hands-on introduction to neural networks and deep learning with an emphasis on computer vision applications. Topics include: linear classifiers; multi-layer neural networks; back-propagation and stochastic gradient descent; convolutional neural networks and their applications to object detection and dense image labeling; recurrent neural networks and state-of-the-art sequence models like transformers; generative adversarial networks and variational autoencoders for image generation; and deep reinforcement learning. Coursework will consist of programming assignments in a common deep learning framework. Those registered for 4 credit hours will have to complete a project. 3 undergraduate hours. 4 graduate hours. Prerequisite: MATH 241 ; one of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 , or BIOE 210 ; CS 225 or equivalent; one of CS 361 , ECE 313 , MATH 461 or STAT 400 . No previous exposure to machine learning is required.

CS 445   Computational Photography   credit: 3 or 4 Hours.

Computer vision techniques to enhance, manipulate, and create media from photo collections, such as panoramic stitching, face morphing, texture synthesis, blending, and 3D reconstruction. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 , MATH 225 , and MATH 231 .

CS 446   Machine Learning   credit: 3 or 4 Hours.

Principles and applications of machine learning. Main paradigms and techniques, including discriminative and generative methods, reinforcement learning: linear regression, logistic regression, support vector machines, deep nets, structured methods, dimensionality reduction, k-means, Gaussian mixtures, expectation maximization, Markov decision processes, and Q-learning. Application areas such as natural language and text understanding, speech recognition, computer vision, data mining, and adaptive computer systems, among others. Same as ECE 449 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 ; One of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 or BIOE 210 ; one of CS 361 , STAT 361 , ECE 313 , MATH 362 , MATH 461 , MATH 463 , STAT 400 or BIOE 310 .

CS 447   Natural Language Processing   credit: 3 or 4 Hours.

Part-of-speech tagging, parsing, semantic analysis and machine translation. Relevant linguistics concepts from morphology (word formation) and lexical semantics (the meaning of words) to syntax (sentence structure) and compositional semantics (the meaning of sentences). 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: One of CS 173 or MATH 213 ; CS 225 ; CS 374 or ECE 374 ; one of CS 361 , STAT 361 , ECE 313 , MATH 362 , MATH 461 , MATH 463 , STAT 400 or BIOE 310 ; one of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 or BIOE 210 .

CS 448   Audio Computing Laboratory   credit: 3 or 4 Hours.

Computational foundations of modern audio applications: theory of audio processing for implementation of applications such as room and 3D/virtual audio rendering, pitch manipulations and autotuning, denoising for communications and forensics, audio classification, music information retrieval based on audio, rudimentary speech recognition, speech and audio coding, applications of machine learning to audio scene recognition, audio restoration, missing data recovery, and many more. This will be a lab-like course in which students collectively implement a variety of core audio operations that are commonplace today. Students will be required to bring to class their laptops and headphones to participate in lab exercises. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: One of CS 240, CS 241, CS 340 , CS 341 , or ECE 391 ; one of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 , or BIOE 210 .

CS 450   Numerical Analysis   credit: 3 or 4 Hours.

Linear system solvers, optimization techniques, interpolation and approximation of functions, solving systems of nonlinear equations, eigenvalue problems, least squares, and quadrature; numerical handling of ordinary and partial differential equations. Same as CSE 401 , ECE 491 , and MATH 450 . 3 undergraduate hours. 3 or 4 graduate hours. Credit is not given toward graduation for both CS 450 and CS 457. Prerequisite: One of CS 101 , CS 124 or CS 125 ; one of CS 357 , MATH 257 , MATH 357 , MATH 415 , or MATH 416 ; MATH 285 .

CS 460   Security Laboratory   credit: 3 or 4 Hours.

Operating systems security: access control, least privilege mechanism and malware techniques. Network security: firewalls, sniffing, tunnels, intrusion detection, AAA and worm structure. System security: forensics security architectures, and attack/defend exercises. Complements CS 461 via hands-on project. Same as ECE 419 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 461 or ECE 422 .

CS 461   Computer Security I   credit: 4 Hours.

Fundamental principles of computer and communications security and information assurance: ethics, privacy, notions of threat, vulnerabilities, and risk in systems, information warfare, malicious software, data secrecy and integrity issues, network security, trusted computing, mandatory and discretionary access controls, certification and accreditation of systems against security standards. Security mechanisms: authentication, auditing, intrusion detection, access control, cryptography, security protocols, key distribution. Same as ECE 422 . 4 undergraduate hours. 4 graduate hours. Prerequisite: One of CS 241, CS 341 or ECE 391 , or a combination of CS 233 and CS 340 .

CS 463   Computer Security II   credit: 3 or 4 Hours.

Program security, trusted base, privacy, anonymity, non-interference, information flow, confinement, advanced auditing, forensics, intrusion detection, key management and distribution, policy composition and analysis, formal approaches to specification and verification of secure systems and protocols, and topics in applied cryptography. Same as ECE 424 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 461 . Recommended: CS 475 .

CS 464   Topics in Societal and Ethical Impacts of Computer Technology   credit: 3 Hours.

Topics selected from key current areas of impact of computer technology on aspects of society and ethics such as: freedom versus the rule of law in cyberspace; social discourse; privacy; livelihoods and automation; fairness; security; political change; business models; technology divide. 3 undergraduate hours. No graduate credit. May be repeated if topics vary. Credit is not given towards a degree from multiple offerings of this course, if those offerings have significant overlap, as determined by the CS department. Prerequisite: CS 225 . One of CS 210 or CS 211 or ECE 316 or PHIL 316 . One of CS 361 or STAT 400 or STAT 200 . Restricted to students with senior standing.

CS 465   User Interface Design   credit: 4 Hours.

A project-focused course covering fundamental principles of user interface design, implementation, and evaluation. Small teams work on a term-long project that involves: analysis of the problem domain, user skills, and tasks; iterative prototyping of interfaces to address user needs; conducting several forms of evaluation such as cognitive walkthroughs and usability tests; implementation of the final prototype. Non-technical majors may enroll as non-programmers who participate in all aspects of the projects with the possible exception of implementation. 4 undergraduate hours. 4 graduate hours. Prerequisite: CS 225 .

CS 466   Introduction to Bioinformatics   credit: 3 or 4 Hours.

Algorithmic approaches in bioinformatics: (i) biological problems that can be solved computationally (e.g., discovering genes, and interactions among different genes and proteins); (ii) algorithmic techniques with wide applicability in solving these problems (e.g., dynamic programming and probabilistic methods); (iii) practical issues in translating the basic algorithmic ideas into accurate and efficient tools that biologists may use. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 .

CS 467   Social Visualization   credit: 3 or 4 Hours.

Visualizing social interaction in networked spaces: investigation of patterns in networked communications systems such as messaging (email, instant messaging), social networking sites and collaborative sites; social network theory and visualizations; exploration of how to move beyond existing visualization techniques; visualizing the network identity over compilations of online data. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 .

CS 468   Tech and Advertising Campaigns   credit: 3 Hours.

Same as ADV 492 . See ADV 492 .

CS 469   Computational Advertising Infrastructure   credit: 3 or 4 Hours.

Thorough coverage of technologies including web-search, auctions, behavioral targeting, mechanisms for viral marketing, that underpin the display of advertisements on a variety of locations (e.g., ads on search engines; display ads). Students shall also learn about emerging areas in computational advertising including location-based adverting and algorithmic synthesis of personalized advertisements. Discussion around privacy will be a significant focus of the class. Same as ADV 462 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 173 ; CS 225 ; one of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 or BIOE 210 ; one of CS 361 , STAT 361 , ECE 313 , MATH 362 , MATH 461 , MATH 463 or STAT 400 .

CS 470   Social and Information Networks   credit: 3 or 4 Hours.

Social networks, auctions, and stock-markets appear to be very different phenomena, but they share a common foundation—the science of networks. The learning goal: to provide a broad, accessible introduction to the foundations of network science. We shall draw on ideas from mathematical sociology, and from game theory to understand strategic interaction over networks. We shall develop algorithms to identify network properties, and models for explaining network dynamics, including viral behavior. 3 undergraduate hours. 4 graduate hours. Prerequisite: CS 225 ; CS 173 ; one of CS 361 , STAT 361 , ECE 313 , MATH 362 , MATH 461 , MATH 463 or STAT 400 ; one of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 , or BIOE 210 .

CS 473   Algorithms   credit: 4 Hours.

Design and analysis techniques, approximation algorithms, randomized algorithms and amortized analysis, and advanced topics such as network flow, linear programming, and dynamic data structures, among others. Same as CSE 414 and MATH 473 . 4 undergraduate hours. 4 graduate hours. Prerequisite: CS 374 or ECE 374 , and one of CS 361 , STAT 361 , ECE 313 , MATH 362 , MATH 461 , MATH 463 or STAT 400 .

CS 474   Logic in Computer Science   credit: 3 or 4 Hours.

An introduction to mathematical logic from the perspective of computer science, emphasizing both computable aspects of logic, especially automated reasoning, as well as applications of logic to computer science in artificial intelligence, databases, formal methods, and theoretical computer science. Prepares students to use logic as a formal tool to solve problems in computer science and AI. Topics include: syntax, semantics and proofs for propositional and first order logic, the decidable logics and efficient realizations in terms of SAT/SMT solvers, decision and semi-decision procedures for first order logic and several first order logic theories, finite model theory and descriptive complexity. Additional topics include: connections between logic and machine learning, higher order logics, applications of logic to program verification, applications of logic in knowledge representation, proof complexity, modal and temporal logic, Courcelle’s theorem and its applications in parameterized complexity, logic and its role in automatic program synthesis, connections between logic and database query languages. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 173 ; CS 225 ; one of CS 374 or ECE 374 .

CS 475   Formal Models of Computation   credit: 3 or 4 Hours.

Finite automata and regular languages; pushdown automata and context-free languages; Turing machines and recursively enumerable sets; linear-bounded automata and context-sensitive languages; computability and the halting problem; undecidable problems; recursive functions; Chomsky hierarchy; computational complexity. Same as MATH 475 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 374 or ECE 374 .

CS 476   Program Verification   credit: 3 or 4 Hours.

Formal methods for demonstrating correctness and other properties of programs. Invariant assertions; Hoare axiomatics; well-founded orderings for proving termination; structural induction; computational induction; data structures; parallel programs; overview of predicate calculus. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 ; one of CS 374 , ECE 374 or MATH 414 .

CS 477   Formal Software Development Methods   credit: 3 or 4 Hours.

Mathematical models, languages, and methods for software specification, development, and verification. Same as ECE 478 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 ; one of CS 374 , ECE 374 or MATH 414 .

CS 481   Advanced Topics in Stochastic Processes & Applications   credit: 3 or 4 Hours.

Same as IE 410 . See IE 410 .

CS 482   Simulation   credit: 3 or 4 Hours.

Same as IE 413 . See IE 413 .

CS 483   Applied Parallel Programming   credit: 4 Hours.

Same as CSE 408 and ECE 408 . See ECE 408 .

CS 484   Parallel Programming   credit: 3 or 4 Hours.

Techniques for the programming of all classes of parallel computers and devices including shared memory and distributed memory multiprocessors, SIMD processors and co-processors, and special purpose devices. Key concepts in parallel programming such as reactive and transformational programming, speculation, speedup, isoefficiency, and load balancing. Synchronization primitives, libraries and languages for parallel programming such as OpenMP and MPI, performance monitoring, program tuning, analysis and programming of numerical and symbolic parallel algorithms. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 241 or CS 341 .

CS 491   Seminar   credit: 0 to 4 Hours.

Seminar on topics of current interest as announced in the Class Schedule. 0 to 4 undergraduate hours. 0 to 4 graduate hours. Approved for S/U grading only. May be repeated in the same or separate terms if topics vary to a maximum of 4 hours. Prerequisite: As specified for each topic offering, see Class Schedule or departmental course description.

CS 492   Senior Project I   credit: 3 Hours.

First part of a project course in computer science. Students work in teams to solve typical commercial or industrial problems. Work involves planning, design, and implementation. Extensive oral and written work is required both on-campus and possibly off-campus at sponsors' locations. CS 492 must be taken as a sequence with either CS 493 or CS 494 . 3 undergraduate hours. No graduate credit. Credit is not given for both CS 492 and a project course in another engineering department for the same project. Prerequisite: For Computer Science majors with senior standing.

CS 493   Senior Project II, ACP   credit: 3 Hours.

Continuation of CS 492 . Identical to CS 494 except for an additional writing component. See CS 494 . 3 undergraduate hours. No graduate credit. Credit is not given for both CS 493 and a project course in another engineering department for the same project. Prerequisite: CS 492 . This course satisfies the General Education Criteria for: Advanced Composition

CS 494   Senior Project II   credit: 3 Hours.

Continuation of CS 492 . 3 undergraduate hours. No graduate credit. Credit is not given for both CS 494 and a project course in another engineering department for the same project. Prerequisite: CS 492 .

CS 497   CS Team Project   credit: 1 to 3 Hours.

Student teams work with CS faculty to complete a significant project requiring advanced knowledge of CS principles. Project topics vary. 1 to 3 undergraduate hours. No graduate credit. May be repeated in the same term up to 6 hours, if topics vary; may be repeated in separate terms. Prerequisite: For majors only; junior or senior standing required.

CS 498   Special Topics   credit: 1 to 4 Hours.

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. 1 to 4 undergraduate hours. 1 to 4 graduate hours. May be repeated in the same or separate terms if topics vary.

CS 499   Senior Thesis   credit: 3 Hours.

Research and thesis development experience in computer science under guidance of a faculty member. Literature search, oral presentation, analysis and implementation, paper preparation, and completion of a written thesis. 3 undergraduate hours. No graduate credit. May be repeated to a maximum of 6 hours. Prerequisite: Consent of instructor. This course satisfies the General Education Criteria for: Advanced Composition

CS 500   Current Topics in Computing Education Research   credit: 4 Hours.

Current research topics and theories in Computers and Education with an emphasis on learning theories such as constructivism, behaviorism, cognitivism, knowledge-in-pieces, test-potentiated learning, and transfer of learning. These theories will be applied to understanding how students learn computing topics such as programming and theoretical computing. These topics will be applied through the design of ethically responsible educational research studies. The course will culminate in students writing a research proposal or conference-style research paper based upon pilot data. 4 graduate hours. No professional credit. May be repeated if topics vary. Credit towards a degree from multiple offerings of this course is not given if those offerings have significant overlap, as determined by the CS department.

CS 507   Topics in Cryptography   credit: 4 Hours.

Modern cryptography helps realize a variety of tasks: from computations on and proofs about secret data, to verifiably offloading computation to untrusted clients, to making programs unintelligible while preserving functionality, to testing untrusted quantum devices. Covers a selection of such cutting-edge topics in cryptography. We will understand how any adversary that counters the security of modern protocols can be transformed into an adversary that contradicts basic mathematical assumptions. We will understand key ideas in recent cryptography research and identify new directions and problems for the future. 4 graduate hours. No professional credit. May be repeated, up to 8 hours in a single term, to a total of 16 graduate hours, if topics vary. Credit is not given towards a degree from multiple offerings of this course if those offerings have significant overlap, as determined by the CS department. Prerequisite: Offerings in separate semesters may specify additional prerequisites each term, depending on the specific topic offered. See section information for additional details.

CS 508   Manycore Parallel Algorithms   credit: 4 Hours.

Same as ECE 508 . See ECE 508 .

CS 510   Advanced Information Retrieval   credit: 4 Hours.

Advanced concepts, models, and algorithms in information retrieval and major recent developments in the field, including historical milestones in information retrieval research, evaluation methodology, vector space retrieval model, probabilistic retrieval models, learning to rank algorithms, probabilistic topic models, information retrieval systems, text analytics, and topics of research frontiers in information retrieval. 4 graduate hours. No professional credit. Prerequisite: One of CS 410 , CS 412 , CS 446 , ECE 449 or LING 406 .

CS 511   Advanced Data Management   credit: 4 Hours.

Advanced concepts in data management and information system design and implementation, and recent developments in the field. 1) Relational roots, objects and extensibility, query languages, data indexing, query processing, transaction processing, benchmarks, and 2) semi-structured data and unstructured data, information extraction, information integration, web search and mining, and other emerging directions in the field. Prerequisite: CS 411 .

CS 512   Data Mining Principles   credit: 4 Hours.

An advanced course on principles and algorithms of data mining. Data cleaning and integration; descriptive and predictive mining; mining frequent, sequential, and structured patterns; clustering, outlier analysis and fraud detection; stream data, web, text, and biomedical data mining; security and privacy in data mining; research frontiers. Prerequisite: CS 412 .

CS 513   Theory & Practice of Data Cleaning   credit: 4 Hours.

Same as IS 537 . See IS 537 .

CS 514   Advanced Topics in Network Science   credit: 4 Hours.

We shall discuss classic and recent research in network analysis. Advanced topics include individual decision-making models, game theory, mechanism design, social choice, social signal design, diffusion of behavior on a network, choice architecture, network models, network mining algorithms and applications. 4 graduate hours. No professional credit. May be repeated if topics vary. Credit is not given towards a degree from multiple offerings of this course, if those offerings have significant overlap, as determined by the CS department. Prerequisite: CS 412 ; one of CS 446 or ECE 449 . Additional prerequisites or corequisites may be specified each term. See section information.

CS 519   Scientific Visualization   credit: 4 Hours.

Visualization techniques useful in analysis of engineering and scientific data. Physical models; methods of computational science; two- and three-dimensional data types; visual representation schemes for scalar, vector, and tensor data; isosurface and volume visualization methods; visual monitoring; interactive steering. Same as CSE 527 . 4 graduate hours. No professional credit. Prerequisite: CS 418 or CSE 427 .

CS 521   Advanced Topics in Programming Systems   credit: 4 Hours.

Advanced topics in building and verifying software systems, selected from areas of current research such as: model checking and automated verification, testing and automated test generation, program synthesis, runtime verification, machine learning and its applications in the design of verified systems, formal analysis of machine learning algorithms, principles of programming languages and type systems. 4 graduate hours. No professional credit. May be repeated if topics vary. Credit is not given towards a degree from multiple offerings of this course if those offerings have significant overlap, as determined by the CS department. Prerequisite: CS 374 or ECE 374 ; CS 421 . Additional prerequisites or corequisites may be specified each term. See section information.

CS 522   Programming Language Semantics   credit: 4 Hours.

Theory of programming languages including functional programming, meta-circular interpreters, typed, untyped and polymorphic lambda-calculi, and denotational semantics. Prerequisite: CS 422 and CS 426 .

CS 523   Advanced Operating Systems   credit: 4 Hours.

Advanced concepts in operating system design and coverage of recent research directions. Resource management for parallel and distributed systems. Interaction between operating system design and computer architectures. Process management, virtual memory, interprocess communication, context switching, parallel and distributed file system designs, persistent objects, process and data migration, load balancing, security, protection. Term projects. 4 graduate hours. No professional credit. Prerequisite: One of CS 423 or CSE 423 ; one of CS 425 or ECE 428 ; and one of CS 433 or CSE 422 .

CS 524   Concurrent Progrmg Languages   credit: 4 Hours.

Theory of concurrency and concurrent programming languages. Formal models of concurrent computation such as process algebras, nets, and actors; high level concurrent programming languages and their operational semantics; methods for reasoning about correctness and complexity of concurrent programs. 4 graduate hours. No professional credit. Prerequisite: CS 422 ; one of CS 475 , MATH 475 or CS 476 .

CS 525   Advanced Distributed Systems   credit: 4 Hours.

Peer-to-peer systems, sensor networks, and fundamental theoretical distributed computing. Review of classical work in each area, and application of design methodologies to explore overlaps across them. Emphasis on protocol design, systems issues, and theory. Reading selections are roughly two-third classical to one-third contemporary. Students write critiques, make presentations, and create a conference paper in a systematic manner. 4 graduate hours. No professional credit. Prerequisite: One of CS 423 , CSE 423 , CS 425 , ECE 428 , CS 438 or ECE 438 .

CS 526   Advanced Compiler Construction   credit: 4 Hours.

Incremental and interactive compiling, error correction, code optimization, models of code generators. Prerequisite: CS 426 .

CS 527   Topics in Software Engineering   credit: 4 Hours.

Fault-tolerant software, software architecture, software patterns, multi-media software, and knowledge-based approaches to software engineering. Case studies. 4 graduate hours. No professional credit. Prerequisite: CS 428 , CSE 429 or CS 429 .

CS 533   Parallel Computer Architecture   credit: 4 Hours.

Theoretical aspects of parallel and pipeline computation; time and processor bounds on classes of computations; data alignment network speed and cost bounds; conflict-free access memories; overall computer system ideas. Same as CSE 522 . 4 graduate hours. No professional credit. Prerequisite: CS 433 or CSE 422 .

CS 534   Advanced Topics in Computer Architecture   credit: 4 Hours.

Current topics in computer architecture, selected from areas of current research such as: energy efficiency, processing in memory, cloud computing, 3-D architectures, security and privacy, hardware and software resiliency, cross-stack design, heterogeneous platforms, approximate computing, mobile computing, machine learning accelerators, and novel computing technologies. 4 graduate hours. No professional credit. May be repeated, if topics vary. Credit is not given towards a degree from multiple offerings of this course, if those offerings have significant overlap, as determined by the CS department. Prerequisite: CS 433 or CSE 422 . Additional prerequisites or co-requisites may be specified each term. See Class Schedule information.

CS 536   Fault-Tolerant Dig Syst Design   credit: 4 Hours.

Same as ECE 542 . See ECE 542 .

CS 537   Advanced Topics in Internet of Things (IoT)   credit: 4 Hours.

Advanced topics in Internet of Things (IoT) algorithms, protocols, architectures, systems, and infrastructures, selected from areas of current research such as: IoT sensors representations and compression, streaming and caching of IoT data, IoT analytics and feature learning, IoT-edge-cloud computing infrastructures, resource optimization for multi-modal IoT systems, applications and human perception of IoT. Students will read and discuss recent research papers and conduct a semester-long research project. 4 graduate hours. No professional credit. May be repeated, if topics vary. Credit towards a degree from multiple offerings of this course is not given if those offerings have significant overlap, as determined by the CS department. Prerequisite: One of CS 425 or ECE 428 ; one of CS 438 or ECE 438 . Additional prerequisites may be specified each term. See section information.

CS 538   Advanced Computer Networks   credit: 4 Hours.

Advanced concepts in computer networks, including congestion control, quality of service, naming, routing, wireless networks, Internet architecture, measurement, network security, and selected recent research directions. 4 graduate hours. No professional credit. Prerequisite: CS 438 or ECE 438 .

CS 539   Distributed Algorithms   credit: 4 Hours.

Same as ECE 526 . See ECE 526 .

CS 540   Deep Learning Theory   credit: 4 Hours.

A rigorous mathematical course covering foundational analyses of the approximation, optimization, and generalization properties of Deep Neural Networks. Topics include: constructive and non-constructive approximations with one hidden layer; benefits of depth; optimization in the NTK regime; maximum margin optimization outside the NTK regime; Rademacher complexity, VC dimensino, and covering number bounds for ReLU networks. Evaluation is primarily based on homeworks, with a smaller project component. The course goal is to prepare students perform their own research in the field. 4 graduate hours. No professional credit. Prerequisite: Basic linear algebra, probability, proof-writing, and statistics required. Real analysis recommended.

CS 541   Computer Systems Analysis   credit: 4 Hours.

Same as ECE 541 . See ECE 541 .

CS 542   Statistical Reinforcement Learning   credit: 4 Hours.

Theory of reinforcement learning, with a focus on sample complexity analyses. Specific topics include MDP basics, finite-sample analyses of online (i.e., exploration) and offline (i.e., batch) RL with a tabular representation, finite-sample analyses of online and offline RL with function approximation, state abstraction theory, off-policy evaluation (importance sampling), and policy gradient. The course goal is to provide a comprehensive understanding of the statistical properties of RL under various settings (e.g., online vs offline), preparing the students for doing research in the area. 4 graduate hours. No professional credit. Prerequisite: Calculus, linear algebra, probability and statistics, and basic concepts of machine learning. Familiarity with (at least one of) the following topics is highly recommended: stochastic processes, numerical analysis, and theoretical computer science.

CS 543   Computer Vision   credit: 4 Hours.

Same as ECE 549 . See ECE 549 .

CS 544   Optimiz in Computer Vision   credit: 4 Hours.

Applications of continuous and discrete optimization to problems in computer vision and machine learning, with particular emphasis on large-scale algorithms and effective approximations: gradient-based learning; Newton's method and variants, applied to structure from motion problems; the augmented Lagrangian method and variants; interior-point methods; SMO and other specialized algorithms for support vector machines; flows and cuts as examples of primal-dual methods; dynamics programming, hidden Markov models, and parsing: 0-1 quadratic forms, max-cut, and Markov random-fields solutions. 4 graduate hours. No professional credit. Prerequisite: One of CS 450 , CSE 401 , ECE 491 , or MATH 450 ; one of CS 473 , CSE 414 or MATH 473 .

CS 545   Machine Learning for Signal Processing   credit: 4 Hours.

Fundamentals of machine learning and signal processing as they pertain to the development of machines that can understand complex real-world signals, such as speech, images, movies, music, biological and mechanical readings, etc. Hands-on examples of how to decompose, analyze, classify, detect and consolidate signals, and examine various commonplace operations such as finding faces from camera feeds, organizing personal music collections, designing speech dialog systems and understanding movie content. 4 graduate hours. No professional credit. Prerequisite: MATH 415 ; one of CS 361 , STAT 361 , MATH 461 , MATH 463 or STAT 400 .

CS 546   Advanced Topics in Natural Language Processing   credit: 4 Hours.

Advanced topics in natural language processing, ranging from general techniques such as deep learning for NLP to specific topics such as information extraction, knowledge acquisition, dialogue systems, language grounding, and natural language generation. Review of classic as well as state-of-the-art techniques and remaining challenges, and exploration of recent proposals for meeting these challenges. Intended for graduate students doing research in natural language processing. 4 graduate hours. No professional credit. May be repeated in separate terms up to 16 hours, if topics vary. Credit towards a degree from multiple offerings of this course is not given if those offerings have significant overlap, as determined by the CS department. Prerequisite: CS 447 and one of CS 446 or ECE 449 , or equivalent background.

CS 547   Deep Learning   credit: 4 Hours.

Same as IE 534 . See IE 534 .

CS 549   Seminar in Cognitive Science   credit: 2 or 4 Hours.

Same as PSYC 514 , ANTH 514 , EPSY 551 , LING 570 , and PHIL 514 . See PSYC 514 .

CS 554   Parallel Numerical Algorithms   credit: 4 Hours.

Numerical algorithms for parallel computers: parallel algorithms in numerical linear algebra (dense and sparse solvers for linear systems and the algebraic eigenvalue problem), numerical handling of ordinary and partial differential equations, and numerical optimization techniques. Same as CSE 512 . 4 graduate hours. No professional credit. Prerequisite: One of CS 450 , CSE 401 , ECE 491 , MATH 450 , CS 457, CS 555 , CSE 510 or MATH 552 .

CS 555   Numerical Methods for PDEs   credit: 4 Hours.

Numerical techniques for initial and boundary value problems in partial differential equations. Finite difference and finite element discretization techniques, direct and iterative solution methods for discrete problems, and programming techniques and usage of software packages. Same as CSE 510 and MATH 552 . 4 graduate hours. No professional credit. Prerequisite: CS 450 , CSE 401 , ECE 491 , MATH 450 or CS 457.

CS 556   Iterative & Multigrid Methods   credit: 4 Hours.

Comprehensive treatment of algebraic and multigrid iterative methods to solve systems of equations, primarily linear equations arising from discretization of partial differential equations. Same as CSE 511 .

CS 558   Topics in Numerical Analysis   credit: 4 Hours.

Advanced topics in numerical analysis selected from areas of current research. Same as CSE 513 . May be repeated. Prerequisite: As specified for each topic offering, see Schedule or departmental course description.

CS 562   Advanced Topics in Security, Privacy, and Machine Learning   credit: 4 Hours.

Advanced topics in security and privacy problems in machine learning systems, selected from areas of current research such as: adversarial machine learning, differential privacy, game theory enabled defenses, robust learning methods, machine learning based cybercrime analysis, network intrusion detection, and malware analysis, and machine learning interpretation techniques. 4 graduate hours. No professional credit. May be repeated if topics vary. Credit is not given towards a degree from multiple offerings of this course if those offerings have significant overlap, as determined by the CS department. Prerequisite: One of CS 446 or ECE 449 and one of CS 463 or ECE 424 , or equivalent courses, by consent of instructor. Additional prerequisites or corequisites may be specified each term. See section information.

CS 563   Advanced Computer Security   credit: 4 Hours.

Current research trends in computer and network security. Privacy, tamper-resistance, unwanted traffic, monitoring and surveillance, and critical infrastructure protection. Subtopics will vary depending upon current research trends. Students work in teams in close coordination with the course instructor to develop one of the topics in depth by carrying out background research and an exploratory project. Same as ECE 524 . 4 graduate hours. No professional credit. Prerequisite: One of CS 461 , CS 463 , ECE 422 or ECE 424 .

CS 565   Human-Computer Interaction   credit: 4 Hours.

In-depth coverage of advanced topics in human-computer interaction (HCI). Applied models of human performance and attention, design tools for creative design tasks, interruptions and peripheral displays, gestures, and bimanual input, and usability evaluation techniques. Students complete a research-oriented term project of their choosing. Prerequisite: CS 465 .

CS 567   Social Signals and Social Media   credit: 4 Hours.

Online social interactions occur in many arenas important to society and human well-being, but are mediated through algorithmic interventions that alter the users expectations in these social spaces. This class explores the presentation of self, the presentation of collectives, the presentation of news, and social dynamics in these online spaces--and how algorithmic intervention shapes them from the perspective of social signaling theory. Topics covered include: the evolution of algorithmic matchmaking (as in online resume/interviews and dating sites), why people share misinformation, the mitigation of trolling, ethics, and bias in social media systems. Upon completion of this course, students will have an up-to-date understanding of the design social media interfaces with incentive structures from social signaling theory. 4 graduate hours. No professional credit. Prerequisite: CS 465 or equivalent or permission of instructor. Prioritize PhD students, then others.

CS 568   User-Centered Machine Learning   credit: 4 Hours.

Introduces guidelines, processes, and systems for designing effective user experiences powered by machine learning models. Topics include design tradeoffs unique to data-driven products and services such as automation versus control, precision versus recall, and personalization versus privacy. Readings from human computer-interaction, product design, cognitive science, machine learning, computer vision, and natural language processing frame in-class design exercises. Students work in teams on a multi-week research project creating or auditing data-driven experiences. 4 graduate hours. No professional credit. Prerequisite: CS 465 or equivalent course work.

CS 571   Combinatorial Mathematics   credit: 4 Hours.

Same as MATH 580 . See MATH 580 .

CS 572   Extremal Graph Theory   credit: 4 Hours.

Same as MATH 581 . See MATH 581 .

CS 574   Randomized Algorithms   credit: 4 Hours.

Basic and advanced concepts in the design and analysis of randomized algorithms. Sampling; concentration inequalities such as Chernoff-Hoeffding bounds; probabilistic method; random walks, dimension reduction; entropy; martingales and Azuma's inequality; derandomization. Randomized algorithms for sorting and searching; graphs; geometric problems. Basics of pseudorandomness and randomized complexity classes. 4 graduate hours. No professional credit. Prerequisite: One of CS 473 , CSE 414 , or MATH 473 ; one of MATH 461 , MATH 463 or STAT 400 .

CS 575   Methods of Combinatorics   credit: 4 Hours.

Same as MATH 584 . See MATH 584 .

CS 576   Topics in Automated Deduction   credit: 2 to 4 Hours.

Advanced topics in computer-aided methods for formal deduction, selected from areas of current research, such as: resolution theorem proving strategies, special relations, equational reasoning, unification theory, rewrite systems, mathematical induction, program derivation, hybrid inference systems, and programming with logic. May be repeated in separate terms. Prerequisite: As specified for each topic offering, see Schedule or departmental course description.

CS 579   Computational Complexity   credit: 4 Hours.

Turing machines; determinism and non-determinism; time and space hierarchy theorems; speed-up and tape compression; Blum axioms; structure of complexity classes NP, P, NL, L, and PSPACE; complete problems; randomness and complexity classes RP, RL, and BPP; alternation, polynomial-time hierarchy; circuit complexity, parallel complexity, NC, and RNC; relativized computational complexity; time-space trade-offs. Same as ECE 579 . 4 graduate hours. No professional credit. Prerequisite: One of CS 473 , CSE 414 , MATH 473 , CS 475 or MATH 475 .

CS 580   Topics in Algorithmic Game Theory   credit: 4 Hours.

A theoretical CS course covering advances in algorithmic game theory. This includes study of strategic, computational, learning, dynamic, and fairness aspects of games and markets (organizations that involves rational and strategic agents). In particular, topics will include computation and complexity of equilibria, mechanism design, fair-division, dynamics in games and markets, price-of-anarchy etc.. These topics arise from applications such as online marketplaces (like Lyft, Uber, eBay, sponsored search, TaskRabbit), social networks, recommendation systems, kidney exchange, spectrum auction, etc., and thereby will prepare students for related research and/or industry jobs. 4 graduate hours. No professional credit. Prerequisite: CS 473 .

CS 581   Algorithmic Genomic Biology   credit: 4 Hours.

Gives each student enough background and training in the area of algorithmic genomic biology so that each will be able to do research in this area, and publish papers. The main focus of the course is phylogeny (evolutionary tree) estimation, multiple sequence alignment, and genome-scale phylogenetics, which are problems that present very interesting challenges from a computational and statistical standpoint. Time permitting, we will also discuss computational problems in microbiome analysis, protein function and structure prediction, genome assembly, and even historical linguistics. Students will learn the mathematical and computational foundations in these areas, read the current literature, and do a team research project. The course is designed for doctoral students in computer science, computer engineering, bioengineering, mathematics, and statistics, and does not depend on any prior background in biology. The technical material will depend on discrete algorithms, graph theory, simulations, and probabilistic analysis of algorithms. 4 graduate hours. No professional credit. Prerequisite: One of CS 374 or ECE 374 and one of CS 361 or STAT 361 , or consent of instructor.

CS 582   Machine Learning for Bioinformatics   credit: 4 Hours.

This graduate course on bioinformatics introduces a selection of topics in computational biology and bioinformatics, with special emphasis on current problems in regulatory genomics and systems biology. Computational approaches discussed will focus on Machine Learning techniques such as Bayesian inference, graphical models, supervised learning and network analysis. Bioinformatics topics will be introduced through lectures by instructor and research paper presentations by students, and include regulatory sequence analysis, cistromics, epigenomics, regulatory network reconstruction, non-coding variant interpretation, and protein structure and function prediction. A research project involving real data analysis with techniques related to course content is mandatory and will help prepare students for bioinformatics research. 4 graduate hours. No professional credit. Prerequisite: CS 446 or ECE 449 ; Credit or concurrent enrollment in CS 466 ; or consent of instructor.

CS 583   Approximation Algorithms   credit: 4 Hours.

Approximation algorithms for NP-hard problems. Basic and advanced techniques in approximation algorithm design: combinatorial algorithms; mathematical programming methods including linear and semi-definite programming, local search methods, and others. Algorithms for graphs and networks, constraint satisfaction, packing and scheduling. 4 graduate hours. No professional credit. Prerequisite: CS 573, CSE 515 or consent of instructor.

CS 584   Embedded System Verification   credit: 4 Hours.

Same as ECE 584 . See ECE 584 .

CS 585   Hardware Verification   credit: 4 Hours.

Same as ECE 519 . See ECE 519 .

CS 586   Combinatorial Optimization   credit: 4 Hours.

Same as IE 519 . See IE 519 .

CS 588   Autonomous Vehicle System Engineering   credit: 4 Hours.

Will introduce students to the computational principles involved in autonomous vehicles, with practical labwork on an actual vehicle. Sensing topics will include vision, lidar and sonar sensing, including state-of-the-art methods for detection, classification, and segmentation. Bayesian filtering methods will be covered in the context of both SLAM and visual tracking. Planning and control topics will cover vehicle dynamics models, state-lattice planning, sampling-based kinodynamic planning, optimal control and trajectory optimization, and some reinforcement learning. Evaluation will involve ambitious challenge projects implemented on a physical vehicle. 4 graduate hours. No professional credit. Prerequisite: CS 374 , ECE 374 , ECE 484 , or equivalent.

CS 591   Advanced Seminar   credit: 0 to 4 Hours.

Seminar on topics of current interest as announced in the Class Schedule. Approved for S/U grading only. May be repeated in the same or separate terms if topics vary. Prerequisite: As specified for each topic offering, see Class Schedule or departmental course description.

CS 597   Individual Study   credit: 2 to 16 Hours.

Individual study or reading in a subject not covered in normal course offerings. May be repeated. Prerequisite: Consent of instructor.

CS 598   Special Topics   credit: 2 to 4 Hours.

CS 599   Thesis Research   credit: 0 to 16 Hours.

Approved for S/U grading only. May be repeated.

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University Library

How to Find Dissertations and Theses

• Finding Dissertations and Theses

Finding Electronic Dissertations in IDEALS

Finding dissertations through the library catalog, proquest dissertations and theses, thesis and serial files (card catalog).

• Non-Illinois Dissertations
• Dissertation Tracking

Ask a Librarian

The Illinois Digital Environment for Access to Learning and Scholarship ( IDEALS ) is the University's digital repository of research outputs and scholarly content produced by the University of Illinois Urbana-Champaign community. Since Fall 2010, all graduate students depositing a dissertation or thesis to the Graduate College are required to do so electronically. After approval and degrees are conferred, the dissertations and theses are made available in IDEALS . Scholarly Communication & Publishing, the U of I Library unit which administers IDEALS, has an excellent online guide to searching or browsing for materials there.

IDEALS Policies

Dissertations from Fall 2010-Present

Because these are official student records, some of the IDEALS policies and procedures do not apply to these documents. These are:

• Withdrawal policy : Dissertations and theses may not be withdrawn except in extremely exceptional circumstances governed by the Graduate College.
• Updating documents : Authors and faculty advisers may not update or change the dissertation or thesis once it has been deposited in IDEALS except in extremely exceptional circumstances governed by the Graduate College.
• Access Restrictions : Authors and faculty advisers may not directly request an access restriction through IDEALS. These requests must be made by the author to the Graduate College.
• Metadata Updates : Small corrections to metadata such as corrections to spelling errors may be made through a request in IDEALS. Per communication with the Graduate College, metadata is not considered part of the academic record. However, substantial changes should still be avoided if unnecessary. Metadata records can also be updated to include links to related items, such as a corresponding dataset in the Illinois Data Bank.

To contact the Graduate College regarding any of these policies, please email [email protected] .

Dissertations and Theses Prior to 2010:

IDEALS and the University Library are actively digitizing dissertations and theses that were originally deposited in paper. Unless we have the agreement of the author or the thesis or dissertation was published as a technical report or working paper series by a university department, these dissertations and theses are restricted to those with an active NetID and password issued by the University of Illinois at Urbana-Champaign.

Dissertations and theses themselves may not be updated or changed.

If you have authored a dissertation or thesis in the past at the University of Illinois and would like to see it appear here or would like to open access to a dissertation or thesis already made available here, please fill out the Request to Make Dissertation or Thesis Available in IDEALS form or contact [email protected] for more information.

In theory, the Library should have at least one copy of all deposited Illinois dissertations prior to 2010. Most non-circulating archival copies (prior to 2010) are kept in the University Archives , with a circulating copy located in other libraries across campus.

To search for specific dissertations, use the Advanced Search in our online catalog.

• Enter your search terms to browse by keywords.
• If you are looking for a specific title, use the drop-down menu on the filters to search by title or author
• Under Material Type, select the last option, Dissertations

You can look for electronic and print copies of dissertations concurrently through the library catalog.

• Use the Advanced search to search by keyword, title, or author. Leave Material Type as all items.
• Use the filters on the left to limit by Resource type, check Dissertations (print copies) and Text Resources (electronic copies)

• ProQuest Dissertations and Theses This link opens in a new window PQDT is a collection of citations to dissertations and theses worldwide from 1861 to the present day. Full-text is available for most of the dissertations added since 1997 and some of the older graduate works. PQDT is also the official digital dissertations archive for the Library of Congress. Also included are the citations to British and Irish dissertations and theses (PQDT: UK & Ireland) since 1761 and abstracts for content since 1986. Note: UIUC masters theses are not in PQDT. They are only found in IDEALS or in the library catalog .

Here is an example of a subject search for education dissertations written at Illinois between 2000 and 2004:

• In the first search box, type University of Illinois at Urbana-Champaign and select University/Institution - SCH from the dropdown menu at the right. ( Note: The full name of the school needs to be typed out, so "University of Illinois" or "UIUC" will not work.) 
• About midway down the page, in the box that says "Subject heading (all)," type "Education."
• To limit by a range of years, select "Specific Date Range..." from Publication Date
• Type in the years 2000 to 2004 in the Start Year and End Year boxes.
• Click the Search button.

If you're unable to locate a pre-1985 U of I dissertation in the online catalog or in a database, search the physical thesis file that is in the card catalog on the second floor of the Main Library.

The card catalog holds records for the archival copies of all theses and dissertations on deposit in the library from 1873 until the beginning of 1984. It is also useful when searching without a complete citation.

Visit the Information Desk for assistance using the card catalog or for help finding dissertations in general. For more information, see this guide on how to use the card catalog .

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• Next: Non-Illinois Dissertations >>
• Last Updated: May 9, 2024 2:48 PM
• URL: https://guides.library.illinois.edu/finddissertationstheses

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James Scholar in Computer Science Majors

Computer Science in Grainger College of Engineering

Computer Science Special Requirements

For the James Scholar Honors Contract, students in the Department of Computer Science have the following options to fulfill the requirements:

• Successfully complete two or three CS electives, numbered 405-498 (excluding CS 491), beyond those required for graduation, and complete one or two semesters of CS 499 Senior Thesis, for a minimum of 12 hours. The CS 499 hours may overlap with Technical Elective requirements for the major; the two or three additional 400-level CS electives may not overlap with any requirements for the major.
• A student may use a dual degree or minor to satisfy the James Scholars Upperclass Honors Contract, which must be approved by the CS James Scholars honors advisor. Most, but not all, minors are acceptable (for example, the Mathematics minor will be permitted only with additional coursework).

LAS Computer Science James Scholar Requirements

James Scholar requirements for CS students in the College of Liberal Arts and Sciences can be found on the LAS James Scholar page: https://las.illinois.edu/academics/honors/james/requirements .

Class Hierarchy At University of Illinois Urbana-Champaign

Part of the senior thesis of alec mori, b.s. '16, computer science, advised by: wade fagen-ulmschneider and cinda heeren, introduction.

Every class at The University of Illinois at Urbana-Champaign (UIUC) is listed in the Course Explorer . As part of the course listing, many courses have prerequisites. This project examines the structure of courses, based on their stated prerequisites, across all courses at UIUC for the Spring 2016 semester.

Understanding the Visualization

Class Hierarchy Visualization

Different Departments - Different Structures

Most of the graph is dominated by the giant structure in the middle, where Math, Physics, Statistics, and Chemistry lie at the heart with fields like Engineering, Computer Science, Psychology, and Economics as appendages. It's interesting to compare that to the fine arts or liberal arts courses, which tend to be less co-dependent. Note that these following graphs come from Fall 2015-Spring 2016, unlike the above graph.

Statistics, Physics, and Computer Science

In this graph, I chose to visualize Statistics, Physics, and Computer Science. Even in these few departments, we can see some inter-departmental dependency (CS 574 (Randomized Algorithms) uses information from STAT 400 (Statistics and Probability I)). Additionally, outside of a few limited classes (mostly independent studies), almost all classes within a department are dependent on one another. Thus, the material from one class tends to propagate forward through other classes in the department.

• CS 225: Data Structures has the most dependencies, where 16 other courses list CS 225 as a prerequisite.
• 294 courses are part of 79 unique clusters , making the average cluster 3.72 courses large .
• CS 125 : Intro to Computer Science
• CS 173 : Discrete Structures
• CS 225 : Data Structures
• CS 233 : Computer Architecture
• CS 421 : Programming Languages & Compilers
• CS 426 : Compiler Construction
• CS 526 : Advanced Compiler Construction

The Fine Arts

For fine arts, I chose to look at Music, Dance, and Theater classes. Even though there are several dense thickets of course dependency, we see that for many more classes that do not require any knowledge from other classes within the major. Additionally we tend to see more clusters amongst the interdependent classes; although there are several groups of courses that are interdependent in these fields, there is not a single class upon which the rest of the department is dependent. For example, THEA 170 (Fundamentals of Acting), THEA 125 (Graphic Skills), and THEA 208 (Dramatic Analysis) all are the basis for many more classes, but each thread of classes they spawn are completely independent.

• THEA 378: Acting Studio II: Acting has the most dependencies, where 8 other courses list THEA 378 as a prerequisite.
• 462 courses are part of 298 unique clusters , making the average cluster 1.55 courses large .
• THEA 170 : Fundamentals of Acting I
• THEA 175 : Fundamentals of Acting II
• THEA 270 : Relationships in Acting I
• THEA 275 : Relationships in Acting II
• THEA 374 : Acting Studio I: Acting
• THEA 378 : Acting Studio II: Acting
• THEA 474 : Acting Studio III: Acting
• THEA 478 : Acting Studio IV: Acting

The Liberal Arts

The liberal arts (here represented by English, History, and Law) are by far the most disjoint the departments. Besides a few instances of "Some Subject I" and "Some Subject II," almost all of the courses within these departments are completely independent. One class's material will seldom be the basis for another class.

• ENGL 200: Intro to the Study of Lit has the most dependencies, where 4 other courses list ENGL 200 as a prerequisite.
• 284 courses are part of 267 unique clusters , making the average cluster 1.06 courses large .
• HIST 200 : Intro Hist Interpretation
• HIST 495 : Honors Research & Writing Sem
• HIST 499 : Thesis Seminar
• HIST 493 : Honors Senior Thesis

These are all language departments across UIUC. The interesting thing about this is how much more it resembles the fine arts than the liberal arts despite being in the college of LAS. However, despite mirroring the fine arts in terms of structure, most of the components are separated into individual departments rather than some small track of a larger department.

• FR 207: Grammar and Composition has the most dependencies, where 4 other courses list FR 207 as a prerequisite.
• 444 courses are part of 198 unique clusters , making the average cluster 2.24 courses large .
• SPAN 122 : Intensive Elementary Spanish
• SPAN 103/130 : Intermediate Spanish
• SPAN 141 : Introduction to Spanish Grammar
• SPAN 204 : Advanced Spanish Grammar in Context
• SPAN 228 : Spanish Composition
• SPAN 250 : Intro to Literary Analysis
• SPAN 312 : Modern Spanish Lit & Cultures
• SPAN 465 : 20th-21st C. Spanish Studies
• SPAN 528 : Sem 20th C. Spanish Lit

Prerequisites

In this matrix, we can see how much each engineering major depends on other sciences (I included Computer Science as as science rather than an engineering here). The color of the squares were darker depending on how many courses within the engineering department depend on a class within the science department. A few interesting trends emerge - for instance, despite the fact that the most people conclude engineering is based around Physics, it seems like traditional Physics is not the baseline for most engineering at UIUC. Theoretical and Applied Mechanics, which uses Physics but is distinctly different than the scientific traditional version, seems to be the basis for more of the engineering. Although Math is unquestionably at the core of several of these departments, it's interesting to see Mechanical Engineering, Aerospace Engineering, and several other disciplines complete ignore Statistics despite topics such as Thermodynamics relying so heavily on it.

Longest Class Chain

The large dots here form one of the longest series of classes that exist at UIUC. Starting at the red dot, they are MATH 112, MATH 115, MATH 220, MATH 231, MATH 241, TAM 210, TAM 211, TAM 251, CEE 380, CEE 483, CEE 484, and CEE 580 (Algebra, Preparation for Calculus, Calculus, Calculus II, Calculus III, Introduction to Statics, Statics, Introductory Solid Mechanics, Geotechnical Engineering, Soil Mechanics and Behavior, Applied Soil Mechanics, and Excavation and Support Systems). That means if you wanted to take CEE 580 as an undergraduate, you would need to enter this school with at least MATH 231 credit, carefully planning your classes each semester to reach CEE 580 in your last semester senior year.

Each small dot represents a class that in some way depends on MATH112.

The Graduate College at the University of Illinois at Urbana-Champaign

Graduate college thesis requirements, organizing your thesis.

The links below will direct you to the Graduate College thesis requirements, as well as sample pages and templates to further guide you in formatting your thesis. When organizing your thesis, be sure to follow the required order, which is shown below.

We also offer basic full-document templates to help you begin formatting your work. You may adapt these templates to fit your needs. If you have issues with formatting your document, please visit our Formatting Tutorials page to access written and video tutorials.

Doctoral Template (MS Word download)

Master's Template (MS Word download)

LaTeX Template (Link to Box folder with files)

Looking for a good example?

The following theses and dissertations passed the Graduate College review with very few, if any, corrections requested by the Thesis Office:

• Civil Engineering
• Crop Sciences
• Electrical and Computer Engineering
• Mathematics
• Mechanical Engineering
• Natural Resources and Environmental Sciences
• Neuroscience (includes supplementary files; see the appendix of the thesis and document uploaded with the thesis)
• Veterinary Clinical Medicine
• Curriculum and Instruction (includes IRB approval letter in an appendix)
• Human Development and Family Studies (includes IRB approval letter in an appendix)

Note the absence of List of Tables and List of Figures sections from the examples above. These sections are generally not necessary, and the Thesis Office advises students who are interested in a quick review with few or no corrections requested to leave these sections out of the thesis or dissertation. Also note that the Table of Contents is most useful for the reader when entries are limited to chapter-level titles only or to chapter-level titles and first-level (main) section headings, as has been done in the examples above.

A note on departmental requirements

This list describes only Graduate College requirements for student theses. The University of Illinois at Urbana-Champaign confers graduate degrees in over 100 units, and many of these departments have additional, discipline-specific format requirements. Students should consult with their program regarding departmental format requirements and departmental thesis review procedures.

Note: The Graduate College Thesis Office will not begin the thesis format review without notification of departmental approval.

• My.SiebelSchool

Senior Design

Each fall, CS Lecturer Michael Woodley welcomes a new group of students to the 2-semester senior projects course (CS 492/4). He invites the students to apply to work on projects with corporate clients, or he matches them with projects for clients in the non-profit sector. Either way, the students begin one of the most  challenging yet valuable classes in their undergraduate career.

According to Woodley, in eight short months, the students will apply years of knowledge to solving a real-world problem. In the process, they will have learned how to: work with colleagues—a typical project has four students—define a problem, develop and test a solution, and communicate their progress to their client.

This past spring, more than 140 students completed 30+ projects—the most ever in the course’s 14-year history. In April, they presented their research results to clients, faculty, and fellow students at the Senior Projects Showcase in Siebel Center. One of those projects, SugarCUB3D , taught children introductory programming concepts and then rewarded them by 3D printing their initials with icing on an Oreo cookie.

Other memorable projects over the years include real-time facial recognition and memory software for Rockwell Collins, a mobile app that enabled cancer patients undergoing treatment to easily schedule help from their friends with meals and other tasks, and geographic information technology that converts map data into games.  

Woodley encourages companies to consider sponsoring one or more projects, especially since students interested in working for a corporate client submit their resumes as part of the matching process at the start of the course.

Interested in sponsoring a CS 492/4 course senior project?

• Visit the Project Sponsor FAQ Page
• Contact Mike Woodley at:  [email protected]
• Cost is $12,000.
• Course runs from late August until mid-April.
• Company representatives pitch their projects to all the enrolled students at the start of the fall semester. After reviewing student resumes, company reps can help select students for their project. 

University of Illinois Urbana-Champaign

Senior Theses - Electrical and Computer Engineering

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Home / News / Students & Campus Life / FSU awards honors medallions to summer 2024 graduates

FSU awards honors medallions to summer 2024 graduates

Florida State University awarded honors medallions to 24 high-achieving graduates during the Aug. 1 Medallion Ceremony in the Nancy B. Marcus Great Hall.

The FSU Honors Program supports the efforts and talents of some of the university’s most intellectually curious students who have the potential, dedication and drive for creating change.

The ceremony included remarks by Michael Furman, associate director of the University Honors Program; Joe O’Shea, associate provost and dean of Undergraduate Studies; Sophie Davis, dual honors graduate; and Jeanne N. Curtin, immediate past chair of the National Board of Directors of the FSU Alumni Association.

The breakdown of the honors awardees is as follows:

16 graduates, University Honors Program Completed the University Honors Program, earning at least 18 honors points in fulfillment of the FSU Honors Program requirements.

4 graduates, Honors in the Major Program Completed an honors thesis in fulfillment of the requirement for the distinction “with honors” in their major.

3 graduates, Dual Honors Completed both the University Honors Program and the Honors in the Major Program.

1 graduate, Outstanding Senior Scholar Completed both the University Honors Program and the Honors in the Major Program, with a 3.9 cumulative GPA or higher.

The honors medallion recipients, along with their hometowns, academic majors and award designations are listed by state and city below:

CONNECTICUT

Nicholas Ilvento, Political Science; International Affairs, University Honors

Elise Miller, Biological Science, University Honors

Sophie Davis, Literature, Media & Culture, Dual Honors

Coral Gables

Owen Landry, Creative Writing; German, Honors in the Major

Thomas Winegarden, Criminology, University Honors

Noelle Knowlton, Editing, Writing & Media, University Honors

Gabriella Behar, Political Science, University Honors

Sofia Gonzalez, Community Patient Care, Dual Honors

Darius Agollari, Biochemistry, Honors in the Major

Savannah Cherepanik, Nursing, University Honors

Orange Park

Liam Woolard, Economics; International Affairs, University Honors

Indira Columbie, Criminology, University Honors

Carson Combs, Economics; Statistics, Outstanding Senior Scholar

Ormond Beach

Maddox Weaver, Economics, University Honors

Urvish Patel, Biological Science, University Honors

Joelyz Wolcott, Behavioral Neuroscience, Honors in the Major

Hannah Lee, Cell & Molecular Neuroscience, University Honors

Tallahassee

Sari-Grace Chaback, Psychology, University Honors

Madeline Feiock, Clinical Professions, University Honors

Sonu Patel, Exercise Physiology, University Honors

Kendall Smith, Biomathematics; Computational Biology (Computer Science), Dual Honors

Olivia Zorrilla, Sport Management, University Honors

Gabriel Valvassori De Jesus Silva, Economics; Finance, Honors in the Major

Sarah Mayer, Nursing; Public Health, University Honors

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Reflections from a Senior in CS

Thought I'd make some closing thoughts on the CS experience at this school for future/current students.

Figure out what the goal of college is for you - to get a job, to get into academia, to strengthen your knowledge in CS, to go out to bars and make lots of friends, or a combination of all/some of these. This will save you lots of time when making decisions. Should you work all night to bump that MP from 85 to a 95, or would you rather go to happies with your friends. Would you sacrifice your grades to make new friends and gain leadership experience in RSOs. If you know your goal, it is relatively simple to make these decisions.

You don't need to know exactly what you want to do within CS, but do not let that be an excuse to do nothing. Don't know if you want to do machine learning, cybersecurity, backend, ui/ux, frontend, product management, or leadership? Doesn't matter. Choose something, and dive deep into it. If you like it, great! If not, move on to the next thing.

Being kind gets you further than being smart. I'm not saying being technically competent isn't important -- it is. but, DO NOT BURN BRIDGES. TALK TO EVERYONE. BE KIND TO EVERYONE. This is especially valuable for freshman. I'm not telling you to be the most outgoing person or spend all your time trying to make random friends just for the sake of it. But when you run into people you met once, say hi! This is very dependent on the type of person you are, and why you are even in college, but in general I notice that people who are just kind and get along with everyone tend to do better in life lol.

If you want to go into further education, do research. or, have connections with some faculty/professors. You cannot get into most masters program without some academic letters of rec, so be a face that some professors know. I graduated with a very high gpa, but didn't apply to a single masters program because I had no connections in the university.

Almost everyone around you is cheating. It is pretty wild how UIUC is ranked so highly with a HUGE proportion of students cheating in classes like Data Structures and Systems Prog. Again, if you know your goal is to just explore computer science topics and expand your knowledge, this wouldn't bother you. However, if your goal in college is to land a high paying job or get into higher education, it will definitely bother you that others are taking easy routes to potentially take your job/college spot. My best advice is to either ignore the issue or join them. Complaining tends to do nothing. I'm sure professors know and don't care, either because they are lazy, or because if you cheat in college you are usually just cheating yourself out of an education.

College isn't designed to be a pipeline to a job. I found myself many times wondering why I'm spending all this time on a course/topics that I won't need in Software Engineering. However, the curriculum is designed to give you a wide breathe of computer science topics, not software engineering topics.

Go out more. Make deep, real connections with people as well as some not-so-deep friendships. Make mistakes, make dumb decisions. Messing up now is way better than messing up in the real world.

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