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.

An introduction to reliable and accurate transmission of information. Entropy, lossless and lossy data compression, optimal compression, information channels, channel capacity, error-correcting codes, and digital fountain codes. Course concepts form the basis for practical applications such as ZIP and MP3 compression, channel coding for DSL lines, communication in deep space and to mobile devices, CDs and disk drives, the development of the Internet, as well as linguistics and human perception.

An introduction to methods for automated learning of relationships on the basis of empirical data. Classification and regression using nearest neighbour methods, decision trees, linear models, and neural networks. Clustering algorithms. Problems of overfitting and of assessing accuracy. Basics of reinforcement learning.

User-centered design of interactive systems. Methodologies, principles, metaphors, task analysis, and other topics. Interdisciplinary design; the role of industrial design and the behavioural sciences. Interactive hardware and software; concepts from computer graphics. Classes of direct manipulation systems, extensible systems, rapid prototyping tools. Additional topics in interactive computational media. Students work on projects in interdisciplinary teams. Enrolment limited, but non-computer scientists welcome. 

(Cross list with MAT302H5) The course will take students on a journey through the methods of algebra and number theory in cryptography, from Euclid to Zero Knowledge Proofs. Topics include: block ciphers and the Advanced Encryption Standard (AES); algebraic and number-theoretic techniques and algorithms in cryptography, including methods for primality testing and factoring large numbers; encryption and digital signature systems based on RSA, factoring, elliptic curves and integer lattices; and zero-knowledge proofs.

Major topics in the development of modern programming languages. Syntax specification, type systems, type inference, exception handling, information hiding, structural recursion, run-time storage management, and programming paradigms. Two non-procedural 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).

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.

Computational methods for solving numerical problems in science, engineering and business. Linear and non-linear equations, approximation, optimization, interpolation, integration and differentiation. The aim is to give students a basic understanding of floating-point arithmetic and the implementation of algorithms used to solve numerical problems, as well as a familiarity with current numerical computing environments. Course concepts are crucial to a wide range of practical applications such as computational finance and portfolio management, graphics and special effects, data mining and machine learning, as well as robotics, bioinformatics, medical imaging and others.

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.

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.

Introduction to computer networks and systems programming of networks. Basic understanding of computer networks and network protocols. Network hardware and software, routing, addressing, congestion control, reliable data transfer, and socket programming.

Introduction to the theory of computability: Turing machines, Church's thesis, computable and non-computable functions, recursive and recursively enumerable sets, reducibility. Introduction to complexity theory: models of computation, P, NP, polynomial time reducibility, NP-completeness, further topics in complexity theory.

Introduction to aspects of parallel programming. Topics include computer instruction execution, instruction-level parallelism, memory system performance, task and data parallelism, parallel models (shared memory, message passing), synchronization, scalability and Amdahl's law, Flynn taxonomy, vector processing and parallel computing architectures.

Principles of operating systems. The operating system as a control program and as a resource allocator. Core topics: processes and threads, concurrency (synchronization, mutual exclusion, deadlock), processor, scheduling, memory management, file systems, and protection.

Standard algorithm design techniques: divide-and-conquer, greedy strategies, dynamic programming, linear programming, randomization, network flows, approximation algorithms and others (if time permits). Students will be expected to show good design principles and adequate skills at reasoning about the correctness and complexity of algorithms.

Robots of the future will need to operate autonomously in unstructured and unseen environments. It is imperative that these systems are built on intelligent and adaptive algorithms. This course will introduce fundamental algorithmic approaches for building an intelligent robot system that can autonomously operate in unstructured environments such as homes and warehouses. This course introduces the broad philosophy of “Sense-Plan-Act”, and covers algorithms in each of these areas -- how should the robot perceive the world, how to make long term decisions and how to perform closed-loop control of articulated robots.

An introduction to robotics covering basic methodologies, tools, and concepts to build a foundation for advanced topics in robotics. The course covers robot manipulators; kinematics; motion planning; and control. Topics covered in lecture will be implemented and explored in a practical environment using robots from different application domains.

This course examines medical robotics from an application driven perspective. Different categories of medical robots and related application principles for therapeutics are considered, with most examples drawn from surgical robotics. How computer methods assist physicians during their use of robotic treatments for patients is a central focus. These computer-assisted methods include treatment planning, patient registration, human-robot interaction, robot control and task execution. Methods will be implemented and explored in a practical environment including the use of real robots.

Theories and algorithms that capture (or approximate) some of the core elements of computational intelligence. Topics include: search, logical representations and reasoning, classical automated planning, representing and reasoning with uncertainty, learning, decision making (planning) under uncertainty. Assignments provide practical experience, in both theory and programming, of the core topics.

Introduction to computing education research (CER) and pedagogical content knowledge. Introduction to learning theories and their application to computing. Foundational and influential CER work. High-impact practices and practical applications to evaluation, assessment, and feedback. This course is writing intensive.

This course involves a significant implementation project in any area of Computer Science. The project may be undertaken individually or in small groups. The project is offered by arrangement with a Computer Science faculty member.

This course involves a significant literature search and expository work in any area of Computer Science. This work must be undertaken individually. It is offered by arrangement with a Computer Science faculty member.

Introduction to a topic of current interest in computer science intended for CSC majors and specialists. Content will vary from year to year. The contact hours for this course may vary in terms of contact type (L, T, P) from year to year, but will be between 24-48 contact hours in total. See the UTM Timetable.

Introduction to a topic of current interest in computer science intended for CSC majors and specialists. Content will vary from year to year. This course may include a practical or tutorial component, depending on the topic chosen for the year. The contact hours for this course may vary in terms of contact type (L, T, P) from year to year, but will be between 24-48 contact hours in total. See the UTM Timetable.

This course provides a richly rewarding opportunity for students in their third or fourth year to work in the research project of a professor in return for 399H 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 Research Opportunity Program (ROP) for more details.

This course provides a richly rewarding opportunity for students in their third or fourth year to work in the research project of a professor in return for 399Y 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 Research Opportunity Program (ROP) for more details.

An introduction to the concepts and techniques for the design and development of electronic games. Topics include: game history, social issues and story elements. The software engineering, artificial intelligence and graphics elements for video games. Level and model design. Audio elements. Real-world aspects of the gaming industry, including the business of game development, design teams and game promotion. Assignments test practical skills in game development, with a team implementation of a complete video game as a course project.

We investigate computation in the large -- utilizing many CPUs with large amounts of memory, large storage and massive connectivity -- to solve computationally complex problems involving big data, serving large collections of users, in high availability, global settings. Our investigation covers both theoretical techniques and current, applied tools used to scale applications on the desktop and in the cloud. Topics include caching, load balancing, parallel computing and models of computation, redundancy, failover strategies, use of GPUs, and noSQL databases.

An introduction to neural networks and deep learning. Backpropagation and automatic differentiation. Architectures: convolutional networks and recurrent neural networks. Methods for improving optimization and generalization. Neural networks for unsupervised and reinforcement learning.

Reinforcement learning is a powerful paradigm for modeling autonomous and intelligent agents interacting with the environment, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This course provides an introduction to reinforcement learning intelligence, which focuses on the study and design of agents that interact with a complex, uncertain world to achieve a goal. We will study agents that can make near-optimal decisions in a timely manner with incomplete information and limited computational resources.

The course will cover Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information-processing approach to the concept of mind and briefly touch on perspectives from psychology, neuroscience, and philosophy.