87 courses in Waterloo’s course catalog for 2020–21 (Fall 2020, Winter 2021, Spring 2021), from UW Open Data. ucalendar asks programs not to read that year’s calendar, so this stands in for it. Original: ucalendar.uwaterloo.ca/2021/COURSE/course-CS.html
CS 100Introduction to Computing through Applications
Using personal computers as effective problem solving tools for the present and the future. Effective use of spreadsheets to process, manipulate, and visualize numeric and textual information. Introduction to the Internet, World Wide Web, HTML, and XML. Algorithms underlying the functional components of web search engines and their influence on data access. Using wikis to publish, reshape, and organize data collaboratively. [Offered: F,W,S]
Prereq: Not open to Mathematics,Biomedical,Chemical,Civil,Computer & Electrical,Environmental,Geological,Management,Mechanical,Mechatronics,Nanotechnology & Systems Design Eng students. Antireq: All second,third or fourth year CS courses or equivalents
An introduction to the fundamentals of computer programming through media computation. Students will learn to write interactive graphical programs. Fundamental language concepts such as variables, conditionals, loops, functions, and arrays. Programming concepts such as coding style, modular design, testing, and debugging. Media concepts such as 2D graphics drawing, input, animation, and image processing. [Offered: F]
A continuation of the introduction to computer programming begun in CS 105. The use of programming, in conjunction with libraries, as a means of solving practical problems in art, design, and data processing. Basic text processing, manipulation of images and sound, handling and visualization of tabular and hierarchical data. Introductions to user interfaces, physical simulation, and object-oriented programming. [Offered: W]
An introduction to the fundamentals of computer science through the application of elementary programming patterns in the functional style of programming. Function definition and application. Tracing via substitution. Design, testing, and documentation. Recursive data definitions. Lists and trees. Functional and data abstraction. [Note: See Note 2 above. Offered: F,W,S]
This course builds on the techniques and patterns learned in CS 115 while making the transition to use of an imperative language. Generative and structural recursion. Mutation (assignment) and its role in an imperative language. Primitive types and basic I/O. Sequencing, selection, looping. Function definition and use. File and console I/O. Issues in computer science. [Offered: F,W,S]
An introduction to the fundamentals of computer science through the application of elementary programming patterns in the functional style of programming. Syntax and semantics of a functional programming language. Tracing via substitution. Design, testing, and documentation. Linear and nonlinear data structures. Recursive data definitions. Abstraction and encapsulation. Generative and structural recursion. Historical context. [Note: See Note 2 above. Offered: F,W]
CS 136Elementary Algorithm Design and Data Abstraction
This course builds on the techniques and patterns learned in CS 135 while making the transition to use an imperative language. It introduces the design and analysis of algorithms, the management of information, and the programming mechanisms and methodologies required in implementations. Topics discussed include iterative and recursive sorting algorithms; lists, stacks, queues, trees, and their application; abstract data types and their implementations. [Note: See Note 2 above. Offered: F,W,S]
Review of fundamental programming concepts and their application. Procedures and parameter passing. Arrays and structures. Recursion. Sorting. Pointers and simple dynamic structures. Space and time analysis of designs. Design methodologies. [Offered: F]
CS 138Introduction to Data Abstraction and Implementation
Software abstractions via elementary data structures and their implementation; encapsulation and modularity; class and interface definitions; object instantiation; recursion; elementary abstract data types, including sequences, stacks, queues, and trees; implementation using linked structures and arrays; vectors and strings; memory models; automatic vs. dynamic memory management. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W]
CS 145 is an advanced-level version of CS 135. [Note: See Note 2 above. This course may be substituted for CS 135 in any degree plan or for prerequisite purposes. Offered: F]
CS 146Elementary Algorithm Design and Data Abstraction (Advanced Level)
CS 146 is an advanced-level version of CS 136. [Note: See Note 2 above. This course may be substituted for CS 136 in any degree plan or for prerequisite purposes. Students who receive a good grade in CS 135 may contact the instructor of CS 146 to seek admission without the formal prerequisites. Offered: W]
Prereq: CS 145 with a grade of at least 75%. Antireq: CS 116, 136, 137, 138.
Important concepts underlying major personal computer application categories; methodologies for learning and evaluating software; operating system and hardware design from the user's point of view, with implications for maintaining a personal computer. Students are encouraged to use their own personal computer for assignments. Social media and the effect of technology on society are explored throughout the course. [Offered: W,S]
CS 230Introduction to Computers and Computer Systems
Basic computer architecture, organization, system services, and software. Typology of processors, memory, I/O devices and their performance. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W,S]
Prereq: One of CS 116, 136, 138, 146; Not open to Computer Science students. Antireq: BME 292/393, CS 241, 251, SYDE 192
The study of the steps required to solve real-world problems on a computer, including problem specification, choice of algorithmic paradigm, analysis, and implementation. Topics include exhaustive search, divide and conquer, greedy, and dynamic programming approaches. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: S]
Top-down design of data structures. Using representation-independent data types. Introduction to commonly used data types, including lists, sets, mappings, and trees. Selection of data representation. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,S]
Introduction to widely used and effective methods of data organization, focusing on data structures, their algorithms, and the performance of these algorithms. Specific topics include priority queues, sorting, dictionaries, data structures for text processing. [Note: Enrolment is restricted; see Note 1 above. Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
CS 240EData Structures and Data Management (Enriched)
Enriched version of CS 240. [Note: See notes 1 and 9 above. CS 240E may be substituted for CS 240 wherever the latter is a requirement. Enrolment is restricted. Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: As permitted by demand and available resources.]
Prereq: A grade of 85% or higher in one of CS 136, 138 or 146; Computer Science students only. Antireq: CS 234, ECE 250
The relationship between high-level languages and the computer architecture that underlies their implementation, including basic machine architecture, assemblers, specification and translation of programming languages, linkers and loaders, block-structured languages, parameter passing mechanisms, and comparison of programming languages. [Note: Enrolment is restricted; see Note 1 above. Lab is not scheduled and students are expected to find time in open hours to complete their work. CS 251 is a recommended corequisite. Offered: F,W,S]
Prereq: (CS 138 or 246) or (a grade of 85% or higher in one of CS 136 or 146); Computer Science and BMath (Data Science) students only. Antireq: CS 230, ECE 351
CS 241EFoundations of Sequential Programs (Enriched)
Enriched version of CS 241. [Note: See notes 1 and 9 above. CS 241E may be substituted for CS 241 wherever the latter is a requirement. Enrolment is restricted. Lab is not scheduled and students are expected to find time in open hours to complete their work. CS 251 is a recommended corequisite. Offered: As permitted by demand and available resources]
Prereq: A grade of 85% or higher in one of CS 136, 138, or 146; Computer Science and BMath (Data Science) students only. Antireq: CS 230, GENE 344
Propositional and predicate logic. Soundness and completeness and their implications. Unprovability of formulae in certain systems. Undecidability of problems in computation, including the halting problem. Reasoning about programs. Correctness proofs for both recursive and iterative program constructions. [Note: Enrolment is restricted; see Note 1 above. Offered: F,W,S]
Enriched version of CS 245. [Note: See notes 1 and 9 above. CS 245E may be substituted for CS 245 wherever the latter is a requirement. Enrolment is restricted. Offered: As permitted by demand and available resources.]
Prereq: A grade of 85% or higher in one of CS 136 or 146; Honours Mathematics students only. Antireq: ECE 208, PMATH 330, and SE 212
Introduction to object-oriented programming and to tools and techniques for software development. Designing, coding, debugging, testing, and documenting medium-sized programs: reading specifications and designing software to implement them; selecting appropriate data structures and control structures; writing reusable code; reusing existing code; basic performance issues; debuggers; test suites. [Note: Enrolment is restricted; see Note 1 above. Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
CS 246EObject-Oriented Software Development (Enriched)
Enriched version of CS 246. [Note: See notes 1 and 9 above. CS 246E may be substituted for CS 246 wherever the latter is a requirement. Enrolment is restricted. Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: As permitted by demand and available resources.]
Prereq: A grade of 85% or higher in one of CS 136 or 146; Honours Mathematics students only. Antireq: SYDE 322
Systematic methods for designing, coding, testing, and documenting medium-sized programs. Major topics include abstraction, modularity, software modeling, object-oriented programming and design, generic programming, testing and debugging. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: S]
Overview of computer organization and performance. Basics of digital logic design. Combinational and sequential elements. Data representation and manipulation. Basics of processor design. Pipelining. Memory hierarchies. Multiprocessors. [Note: Students enrolled in Digital Hardware Specialization should enrol in ECE 222. Enrolment is restricted; see Note 1 above. Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
Prereq: One of CS 136, 138, 146; Computer Science and BMath (Data Science) students only. Antireq: BME 292/393, ECE 222, ME 262, MTE 262, SYDE 192
CS 251EComputer Organization and Design (Enriched)
Enriched version of CS 251. [Note: Students enrolled in Digital Hardware Specialization should enrol in ECE 222. See notes 1 and 9 above. CS 251E may be substituted for CS 251 wherever the latter is a requirement. Enrolment is restricted. Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: As permitted by demand and available resources.]
Prereq: A grade of 85% or higher in one of CS 136 or 146; Computer Science and BMath (Data Science) students only. Antireq: BME 292/393, ECE 222, ME 262, MTE 262, SYDE 192
An introduction to information systems and their strategic role in business. Topics include types of information systems, organizational requirements, systems development strategies, decision support systems, data and information management, and information systems management, control and implementation. [Offered: F,W,S]
Prereq: One of CS 106, 116, 136, 138, 146; Level at least 2B; Not open to Computer Science students. Antireq: AFM 241, BUS 415W, 486W, CS 480/490, MSCI 441
CS 335Computational Methods in Business and Finance
An introduction to numerical methods for business and finance. Floating-point arithmetic, interpolation. Methods for portfolio optimization and contingent-claims valuation. Solution of nonlinear equations. Monte Carlo methods, lattice methods, simulation of hedging strategies. [Offered: F,W]
CS 338Computer Applications in Business: Databases
A user-oriented approach to the management of large collections of data. Methods used for the storage, selection and presentation of data. Common database management systems. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
Prereq: One of CS 230, 231, 234, 246, 330; Not open to Computer Science students. Antireq: CS 348, 448, MSCI 346
The study of efficient algorithms and effective algorithm design techniques. Program design with emphasis on pragmatic and mathematical aspects of program efficiency. Topics include divide and conquer algorithms, recurrences, greedy algorithms, dynamic programming, graph search and backtrack, problems without algorithms, NP-completeness and its implications. [Note: Enrolment is restricted; see Note 1 above. Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
Prereq: CS 240 and (MATH 239 or 249); Computer Science and BMath (Data Science) students only. Antireq: CS 231, ECE 406
An introduction to concurrent and parallel programming, with an emphasis on language constructs. Major topics include: exceptions, coroutines, atomic operations, critical sections, mutual exclusion, semaphores, high-level concurrency, deadlock, interprocess communication, process structuring, shared memory and distributed architectures. Students will learn how to structure, implement and debug concurrent programs. [Note: Enrolment is restricted; see Note 1 above. Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W]
The main objective of this course is to introduce students to fundamentals of database technology by studying databases from three viewpoints: those of the database user, the database designer, and the database administrator. It teaches the use of a database management system (DBMS) by treating it as a black box, focusing only on its functionality and its interfaces. Topics include introduction to database systems, relational database systems, database design methodology, SQL and interfaces, database application development, concept of transactions, ODBC, JDBC, database tuning, database administration, and current topics (distributed databases, data warehouses, data mining). [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
Prereq: Computer Science and BMath (Data Science) students only. Coreq: CS 240. Antireq: CS 338, ECE 356, 456, MSCI 346
An introduction to contemporary user interfaces, including the basics of human-computer interaction, the user interface design/evaluation process, the event abstraction, user interface components, specification of user interfaces, and the architectures within which user interfaces are developed. Implementation and evaluation of a typical user interface is considered. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
Prereq: CS 241 and (one of MATH 115, 136, 146); Computer Science students only
An introduction to the fundamentals of operating system function, design, and implementation. Topics include concurrency, synchronization, processes, threads, scheduling, memory management, file systems, device management, and security. [Note: Enrolment is restricted; see Note 1 above. Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
Models of computers including finite automata and Turing machines. Basics of formal languages with applications to the syntax of programming languages. Alternate characterizations of language classes. Proving unrecognizability. Unsolvable problems and their relevance to the semantics of programming. [Note: Enrolment is restricted; see Note 1 above. Offered: F,W,S]
Prereq: CS 240, 241 and (MATH 239 or 249); Computer Science students only. Antireq: CS 365
Finite automata and regular expressions. Pushdown automata and context-free grammars. Turing machines and undecidability. Time and space complexity. Diagonalization and hierarchies. CS 365 covers the material in CS 360 at an accelerated pace plus additional topics in computational complexity. [Note: CS 365 may be substituted for CS 360 in any degree plan or for prerequisite purposes; enrolment is restricted; see Note 1 above. Offered: W]
Prereq: CS 240, 241 and (MATH 239 or 249); Computer Science students only. Antireq: CS 360
Principles and practices of basic numerical computation as a key aspect of scientific computation. Visualization of results. Approximation by splines, fast Fourier transforms, solution of linear and nonlinear equations, differential equations, floating point number systems, error, stability. Presented in the context of specific applications to image processing, analysis of data, scientific modeling. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
A rigorous introduction to the field of computational mathematics. The focus is on the interplay between continuous models and their solution via discrete processes. Topics include pitfalls in computation, solution of linear systems, interpolation, discrete Fourier transforms, and numerical integration. Applications are used as motivation. [Note: This course may be substituted for CS 370 in any degree plan or for prerequisite purposes; lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W,S]
An upper-level studio course to create computational projects that function as art works and aesthetic experiences. Students will work in interdisciplinary teams to combine computer science principles with fine art technical and conceptual skills. [Offered: W]
An investigation into the role and function of software engineering practice in the construction of computer based systems. Topics include: requirements and specification; documentation techniques; analysis and design; implementation; testing and maintenance; management issues. [Offered: F,W]
Prereq: CS 330; Level at least 3A; Not open to Computer Science students. Antireq: CS 446/ECE 452, SE 464
Introduces non-CS major students to infrastructure for data-intensive analytics, with a focus on abstractions, frameworks, and algorithms that allow developers to distribute computation across many machines. Topics include core concepts (partitioning, replication, locality, consistency), computational models (MapReduce, dataflows, stream processing, bulk-synchronous parallel), and applications. [Offered: F,W]
Prereq: (CS 231 or 234 or 341) and (CS 251 or 330); Not open to Computer Science students. Antireq: CS 451
An introduction to networks, protocols, and distributed systems. Layered models, resource management, naming, addressing and routing, reliable communication, security, and higher-level services. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W]
Prereq: One of CS 230, 241, 246, 251; Not open to Computer Science students. Antireq: CS 454, 456, ECE 428, 454
An exposure to important concepts and issues in contemporary programming languages. Data types, abstraction, and polymorphism. Program structure. Lambda calculus and functional programming, logic programming, object-oriented programming. Semantics of programming languages. Critical comparison of language features and programming methodologies using examples drawn from a variety of programming languages including Lisp, Prolog, ML, Ada, Smalltalk, Icon, APL, and Lucid. Programming assignments involve the use of some of these languages. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W]
CS 445Software Requirements Specification and Analysis
Introduces students to the requirements definition phase of software development. Models, notations, and processes for software requirements identification, representation, analysis, and validation. Cost estimation from early documents and specifications. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W]
Prereq: CS 350; Computer Science students only. Antireq: SE 463
Introduces students to the design, implementation, and evolution phases of software development. Software design processes, methods, and notation. Implementation of designs. Evolution of designs and implementations. Management of design activities. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W,S]
CS 447Software Testing, Quality Assurance and Maintenance
Introduces students to systematic testing of software systems. Software verification, reviews, metrics, quality assurance, and prediction of software reliability and availability. Related management issues. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W]
Prereq: CS 350; Computer Science students only. Antireq: SE 465
The objective of this course is to introduce students to fundamentals of building a relational database management system. The course focuses on the database engine core technology by studying topics such as storage management (data layout, disk-based data structures), indexing, query processing algorithms, query optimization, transactional concurrency control, logging and recovery. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W]
An introduction to the fundamental theories, methods, and research in the design and evaluation of novel computational artifacts designed to meet real-world human needs. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work.]
Prereq: CS 240, 241; Level at least 3B; Computer Science students only. Antireq: SYDE 348/548
The course is intended to provide the student with an appreciation of modern computer design and its relation to system architecture, compiler technology and operating system functionality. The course places an emphasis on design based on the measurement of performance and its dependency on parallelism, efficiency, latency and resource utilization. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W]
Introduces students to infrastructure for data-intensive computing, with a focus on abstractions, frameworks, and algorithms that allow developers to distribute computations across many machines. Topics include core concepts (partitioning, replication, locality, consistency), computational models (MapReduce, dataflows, stream processing, bulk-synchronous parallel), and applications. [Offered: F,W]
Prereq: CS 341, 348 and (CS 350 or SE 350); Computer Science and BMath (Data Science) students only
Intended to give students experience with tools and techniques of real-time programming, this course includes not only issues of microcomputer architecture and a real-time programming language and operating system, but also hands-on experience programming a microcomputer for applications such as process control, data acquisition and communication. [Note: This course involves project work. Offered: W,S]
Prereq: CS 350 or SE 350; Computer Science students only
An introduction to distributed systems, emphasizing the multiple levels of software in such systems. Specific topics include fundamentals of data communications, network architecture and protocols, local-area networks, concurrency control in distributed systems, recovery in distributed systems, and clock synchronization. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W,S]
An introduction to network architectures and protocols, placing emphasis on protocols used in the Internet. Specific topics include application layer protocols, network programming, transport protocols, routing, multicast, data link layer issues, multimedia networking, network security, and network management. [Offered: F,W,S]
Basic techniques of system performance evaluation. Specific topics include: performance modeling, discrete event simulation, verification and validation of simulation models, analysis of simulation output, analysis of single server queue and queueing networks, modeling of computer systems, networks, and other queueing or non-queueing systems. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work.]
Prereq: (CS 246 or 247) and (one of STAT 206, 231, 241); Computer Science students only
Security and privacy issues in various aspects of computing. Specific topics include: comparing security and privacy, program security, writing secure programs, controls against program threats, operating system security, formal security models, network security, Internet application security and privacy, privacy-enhancing technologies, database security and privacy, inference, data mining, security policies, physical security, economics of security, and legal and ethical issues. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
Languages and their representations. Grammars --Chomsky hierarchy. Regular sets and sequential machines. Context-free grammars -- normal forms, basic properties. Pushdown automata and transducers. Operations on languages. Undecidable problems in language theory. Applications to the design of programming languages and compiler construction.
Prereq: CS 360 or 365; Computer Science students only
Algorithmic approaches and methods of assessment that reflect a broad spectrum of criteria, including randomized algorithms, amortized analysis, lower bounds, approximation algorithms, and on-line algorithms. Particular examples will be chosen from different areas of active research and application. [Offered: F,S]
Basic concepts and implementation of numerical linear algebra techniques and their use in solving application problems. Special methods for solving linear systems having special features. Direct methods: symmetric, positive definite, band, general sparse structures, ordering methods. Iterative methods: Jacobi, Gauss-Seidel, SOR, conjugate gradient. Computing and using orthogonal factorizations of matrices. QR and SVD methods for solving least squares problems. Eigenvalue and singular value decompositions. Computation and uses of these decompositions in practice. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: S]
The interaction of financial models, numerical methods, and computing environments. Basic computational aspects of option pricing and hedging. Numerical methods for stochastic differential equations, strong and weak convergence. Generating correlated random numbers. Time-stepping methods. Finite difference methods for the Black-Scholes equation. Discretization, stability, convergence. Methods for portfolio optimization, effect of data errors on portfolio weights. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Students who receive a good grade in CS 335 may contact the instructor of CS 476 to seek admission without the formal prerequisites. Offered: W]
Introduction to modeling and algorithmic techniques for machines to learn concepts from data. Generalization: underfitting, overfitting, cross-validation. Tasks: classification, regression, clustering. Optimization-based learning: loss minimization. regularization. Statistical learning: maximum likelihood, Bayesian learning. Algorithms: nearest neighbour, (generalized) linear regression, mixtures of Gaussians, Gaussian processes, kernel methods, support vector machines, deep learning, sequence learning, ensemble techniques. Large scale learning: distributed learning and stream learning. Applications: Natural language processing, computer vision, data mining, human computer interaction, information retrieval. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
Prereq: CS 341 and (STAT 206 or 231 or 241); Computer Science and BMath (Data Science) students only.
CS 482Computational Techniques in Biological Sequence Analysis
Computer science principles and algorithms in biological sequence analysis. Topics include algorithms for sequence comparison, for large-scale database search in biological databases, for sequence assembly, for evolutionary tree reconstruction, for identifying important features in DNA and RNA sequences, and underlying computational techniques for understanding strings and trees and for making probabilistic inferences. [Offered: W]
Introduction to image and vision understanding by computer. Camera-system geometry, image formation and lighting, and image acquisition. Basic visual processes for recognition of edges, regions, lines, and surfaces. Processing of stereo images, and motion in image sequences. Object recognition. Applications of computer-vision systems. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work.]
CS 485Statistical and Computational Foundations of Machine Learning
Extracting meaningful patterns from random samples of large data sets. Statistical analysis of the resulting problems. Common algorithmic paradigms for such tasks. Central concepts: VC-dimension, margins of a classifier, sparsity and description length, other types of regularization. Performance guarantees: generalization bounds, data dependent error bounds and computational complexity of learning algorithms. Common paradigms: neural networks, kernel methods and support-vector machines, boosting, nearest neighbor classifiers. Applications to data mining.
Prereq: CS 341 and (STAT 206 or 230 or 240); Computer Science and BMath (Data Science) students only
Goals and methods of artificial intelligence. Methods of general problem solving. Knowledge representation and reasoning. Planning. Reasoning about uncertainty. Machine learning. Multi-agent systems. Natural language processing. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S]
Prereq: CS 341; Computer Science and BMath (Data Science) students only. Coreq: STAT 206 or 231 or 241.
An introduction to the use of computers for symbolic mathematical computation, involving traditional mathematical computations such as solving linear equations (exactly), analytic differentiation and integration of functions, and analytic solution of differential equations. [Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W]
Prereq: CS 231 or 234 or 240; Honours Mathematics only
Software and hardware for interactive computer graphics. Implementation of device drivers, 3-D transformations, clipping, perspective, and input routines. Data structures, hidden surface removal, colour shading techniques, and some additional topics will be covered. [Note: This course involves project work. Offered: F,W,S]
The integration of business and technical considerations in the design, implementation and management of information systems. Topics include: IS planning and development; business, management, executive, and strategic information systems, including case studies of selected large- scale systems; decision support systems; end-user training and development; systems security, disaster planning and recovery. Practical examples of information systems in industry. [Offered: W,S]
Prereq: CS 350 or SE 350; Computer Science students only. Antireq: BUS 415W, 486W
This course is designed to consider the problems encountered by individuals, organizations and society as computer technology is adopted, with a view towards assessing possible courses of action. [Offered: W,S]
Students work in teams on substantial open-ended computer science problems as part of the CS 493/494 course sequence. Lectures describe project management fundamentals and ethical and legal issues in computing. Students form teams, select projects, define project goals, perform risk assessment, establish a project plan, and develop a prototype. Possible project topics can include development of software systems, analysis of extensions to existing systems across the field, and experimental computer science.[Offered: F]
Prereq: CS 341, 350; Computer Science students only. Not open to Software Engineering students.
Continuing from CS 493, student teams continue development of their project, update project plans, explore design alternatives, perform testing, and analyze experimental results. Teams prepare and deliver technical presentations and demonstrations of their projects, and analyze ethical and legal aspects of their work. [Offered: W]
Prereq: CS 493; Computer Science students only. Not open to Software Engineering students.
The student will undertake new analysis, synthesis, measurement, or experimentation to produce a document that demonstrates a depth of understanding of a topic that goes beyond what is obtained in a standard undergraduate education. [Note: Offered for CR/NCR only. CS 499R is recommended. A detailed thesis proposal and names of a supervisor and reader are required.]