| source University of Toronto, Mississauga (X) |
level |
department Computer Science (X) |
An introduction to computing for non-computer scientists. History of computing machinery; representation of data and their interaction with operations; hardware, software, operating systems; problem solving and algorithms; social issues in computing; a gentle introduction to programming. This course is an introduction to becoming actively engaged with computing, not a tutorial on using particular computer applications. [
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Structure of computers; the computing environment. Programming in a language such as Python. Program structure: elementary data types, statements, control flow, functions, classes, objects, methods,
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Abstract data types and data structures for implementing them. Linked data structures. Encapsulation and information-hiding. Object-oriented programming. Specifications. Analyzing the efficiency of programs. Recursion. This course assumes programming experience in a language such as Python, C++, or Java, as provided by
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An introduction to software design and development concepts, methods, and tools using a statically-typed object-oriented programming language such as Java. Topics from: version control, build management, unit testing, refactoring, design patterns, advanced IDE usage, regular expressions, markup languages, parsing using finite state machines, and reflection. [
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Software tools and techniques, and their use in the Linux system. What goes on in the operating system when programs are executed. Core topics: software tools, pipes and filters, file processing, process management, system calls, signals, concurrency. The C programming language. Possible other topics: basic network programming, scripting languages. [
Score: 7.930717 Details | Listing | Web page
Mathematical induction; correctness proofs for iterative and recursive algorithms; recurrence equations and their solutions (including the "Master Theorem"); introduction to automata and formal languages. (This course replaces
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Introduction to computer organization and architecture. The view ranges from low-level bits, with Boolean expressions and the associated gates, to higher-level processor and memory structures. The design and analysis of combinational circuits and sequential circuits. The control unit and the datapath. Students will design and implement circuits. [
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Algorithm analysis: worst-case, average-case, and amortized complexity. Standard abstract data types, such as graphs, dictionaries, priority queues and disjoint sets. A variety of data structures for implementing these abstract data types, such as balanced search trees, hashing, heaps and disjoint forests. Design, implementation and comparison of data structures. Introduction to lower bounds. [
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A practical introduction to Computer Science for non-computer scientists.
Score: 7.930717 Details | Listing | Web page
Targeted instruction and significant practice in the communications required for careers in computer science. The curriculum covers written, oral and interpersonal communication. Students will hand in short pieces of writing each week, will make oral presentations several times in the semester, and will work together in simulated project meetings and other realistic scenarios of pair and small group interaction. This can be used to satisfy the writing requirement in CSC programs. [
Score: 7.930717 Details | Listing | Web page
This courses provides a richly rewarding opportunity for students in their second year to work in the research project of a professor in return for 299Y course credit. Students enrolled have an opportunity to become involved in original research, learn research methods and share in the excitement and discovery of acquiring new knowledge. Participating faculty members post their project descriptions for the following summer and fall/winter sessions in early February and students are invited to apply in early March. See
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Privacy and Freedom of Information; recent Canadian legislation and reports. Computers and work; employment levels, quality of working life. Electronic fund transfer systems; transborder data flows. Computers and bureaucratization. Computers in the home; public awareness about computers. Robotics. Professionalism and the ethics of computers. The course is designed not only for science students, but also those in social sciences or humanities. [
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An introduction to agile development methods appropriate for medium-sized teams and rapidly-moving projects. Basic software development infrastructure; requirements elicitation and tracking; estimation and prioritization; teamwork skills; basic UML; design patterns and refactoring; security. [
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An introduction to software development on the web. Concepts underlying the development of programs that operate on the web; survey of technological alternatives; greater depth on some technologies. Operational concepts of the internet and the web, static client content, dynamic client content, dynamically served content, n-tiered architectures, web development processes, and security on the web. Assignments involve increasingly more complex web-based programs. [
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Measuring information. The source coding theorem. Data compression using ad hoc methods and dictionary-based methods. Probabilistic source models, and their use via Huffman and arithmetic coding. Noisy channels and the channel coding theorem. Error correcting codes, and their decoding by algebraic and probabilistic methods. [
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User-centred design of interactive systems; methodologies, principles and metaphors; task analysis. Interdisciplinary design; the role of graphic design, industrial design and the behavioural sciences. Interactive hardware and software; concepts from computer graphics. Typography, layout, colour, sound, video, gesture and usability enhancements. Classes of interactive graphical media; direct manipulation systems, extensible systems, rapid proto-typing tools. Students work on projects in interdisciplinary teams. Enrolment limited, but non-computer scientists welcome. [
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A unified introduction to image synthesis and image analysis aimed at students with an interest in computer graphics, computer vision or the visual arts. Focus on three major topics: (1) visual computing principles - computational and mathematical methods for creating, capturing, analyzing and manipulating digital photographs (raster algorithms, image acquisition, basic image processing, image warping, anti-aliasing); (2) digital special effects - applying these principles to create special effects found in movies and commercials; (3) visual programming - using Java and Swing/Graphics2D or C/C++ and OpenGL to create graphical user interfaces for synthesizing and manipulating photographs. [
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Supervised neural networks: the perceptron learning procedure, the backpropagation learning procedure and its applications. Elaborations of backpropagation: activation and error functions, improving speed and generalization, Bayesian approaches. Associative memories and optimization: Gibbs sampling, mean field search. Representation in neural networks: distributed representations, effects of damage, hierarchical representations. Unsupervised neural networks: competitive learning, Boltzmann machines, sigmoid belief nets. [
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Major topics in the development of modern programming languages. Syntax specification, the evolution of programming languages (including abstract data types and object orientation, and contributions of C++ to language design), design and implementation of subprograms (including parameter passing techniques, and scope and lifetime of variables), run-time storage management (including garbage collection), and programming paradigms. Two nonprocedural programming paradigms: functional programming (illustrated by languages such as Lisp, Scheme, ML or Haskell) and logic programming (illustrated by languages such as Prolog, XSB or Coral). [
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Introduction to the tools and techniques of the digital detective. Electronic discovery of digital data, including field investigation methods of the computer crime scene. Focus on the computer science behind computer forensics, network forensics and data forensics. Forensic topics include: computer structure, data acquisition from storage media, file system analysis, network intrusion detection, electronic evidence, Canadian computer crime case law. [
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The study of numerical methods for solving problems in linear algebra, non-linear equations, approximation, integration and ordinary differential equations. The aim is to give students a basic understanding of both floating-point arithmetic and the methods used to solve numerical problems as well as a familiarity with the types of subroutines found in typical software packages. [
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Theory, tools and techniques of problem analysis for software systems development, covering both information systems and control systems. Topics include: systems theory, requirements specification, analysis of feasibility and risk, object-oriented analysis, business process modeling, modeling and analysis of information structure, modeling and analysis of the behavior of reactive systems, analysis of non-functional requirements, verification and validation, requirements prioritization and software evolution. [
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Introduction to database management systems. The relational data model. Relational algebra. Querying and updating databases: the query language SQL. Application programming with SQL. Integrity constraints, normal forms, and database design. Elements of database system technology: query processing, transaction management. [
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An investigation of many aspects of modern information security. Major topics cover: Techniques to identify and avoid common software development flaws which leave software vulnerable to crackers. Utilizing modern operating systems security features to deploy software in a protected environment. Common threats to networks and networked computers and tools to deal with them. Cryptography and the role it plays in software development, systems security and network security. [
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Simulation and mathematical analysis of models of queueing systems. Concentration on dynamic, stochastic, discrete-event systems. Simulation topics: selecting input probability distributions, generating random numbers and random variates, output data analysis for one or more system configurations, variance reduction techniques. Analysis topics: queueing characteristics, transient and steady-state behaviour, performance measures, the M/M/1 queue in detail, some non-Markovian queues.
Score: 7.930717 Details | Listing | Web page