Mathematical induction with emphasis on applications relevant to computer science. Aspects of mathematical logic, correctness proofs for iterative and recursive algorithms, solutions of linear and divide-and-conquer recurrences, introduction to automata and formal languages.

Principles of the design and operation of digital computers. Binary data representation and manipulation, Boolean logic, components of computer systems, memory technology, peripherals, structure of a CPU, assembly languages, instruction execution, and addressing techniques. There are a number of laboratory periods in which students conduct experiments with digital logic circuits.

Design, analysis, implementation and comparison of efficient data structures for common abstract data types. Priority queues: heaps and mergeable heaps. Dictionaries: balanced binary search trees, B-trees, hashing. Amortization: data structures for managing dynamic tables and disjoint sets. Data structures for representing graphs. Graph searches.

Introduction to software development methodologies with an emphasis on agile development methods appropriate for rapidly-moving projects. Basic software development infrastructure; requirements elicitation and tracking; prototyping; basic project management; basic UML; introduction to software architecture; design patterns; testing.

An introduction to software development on the web. Concepts underlying the development of programs that operate on the web. Operational concepts of the internet and the web, static and dynamic client content, dynamically served content, n-tiered architectures, web development processes and security on the web.

The course will provide an introduction to the field of Human-Computer Interaction (HCI) with emphasis on guidelines, principles, methodologies, and tools and techniques for analyzing, designing and evaluating user interfaces. Subsequent topics include usability assessment of interactive systems, prototyping tools, information search and visualization, mobile devices, social media and social networking, and accessibility factors.

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 and non-linear models, class-conditional models, neural networks, and Bayesian methods. Clustering algorithms and dimensionality reduction. Model selection. Problems of over-fitting and assessing accuracy. Problems with handling large databases.

Major topics in the design, definition, analysis, and implementation of modern programming languages. Study of programming paradigms: procedural (e.g., C, Java, Python), functional (e.g., Scheme, ML, Haskell) and logic programming (e.g., Prolog, Mercury).

An introduction to computational methods for solving problems in linear algebra, non-linear equations, approximation and integration. Floating-point arithmetic; numerical algorithms; application of numerical software packages.

Introduction to database management systems. The relational data model. Relational algebra. Querying and updating databases: the SQL query language. Application programming with SQL. Integrity constraints, normal forms, and database design. Elements of database system technology: query processing, transaction management.

How networks underlie the social, technological, and natural worlds, with an emphasis on developing intuitions for broadly applicable concepts in network analysis. Topics include: introductions to graph theory, network concepts, and game theory; social networks; information networks; the aggregate behaviour of markets and crowds; network dynamics; information diffusion; popular concepts such as "six degrees of separation", the "friendship paradox", and the "wisdom of crowds".

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.
Note: Although the courses CSCC63H3 and CSCC73H3 may be taken in any order, it is recommended that CSCC73H3 be taken first.

Principles of operating systems. The operating system as a control program and as a resource allocator. The concept of a process and concurrency problem: synchronization, mutual exclusion, deadlock. Additional topics include memory management, file systems, process scheduling, threads, and protection.

Standard algorithm design techniques: divide-and-conquer, greedy strategies, dynamic programming, linear programming, randomization, and possibly others.

The course introduces the fundamental principles, problems, and techniques involved in the operation of mobile robots and other automated systems. Course topics include: components of automated systems, sensors and sensor management, signal acquisition and noise reduction, principles of robot localization, FSM-based A.I. for planning, fault-tolerance and building fault-tolerant systems, real-time operation and real-time operating systems; and computational considerations such as hardware limitations and code optimization. Ethical considerations in the implementation and deployment of automated systems are discussed. The concepts covered in the course are put in practice via projects developed on a Lego robotic platform.

An introduction to the theory and practice of large-scale software system design, development, and deployment. Project management; advanced UML; requirements engineering; verification and validation; software architecture; performance modeling and analysis; formal methods in software engineering.

The trade-offs between benefits and risks to society of information systems, and related issues in ethics and public policy. Topics will include safety-critical software; invasion of privacy; computer-based crime; the social effects of an always-online life; and professional ethics in the software industry. There will be an emphasis on current events relating to these topics.

The course will cover in detail the principles and algorithms used to generate high-quality, computer generated images for fields as diverse as scientific data visualization, modeling, computer aided design, human computer interaction, special effects, and video games. Topics covered include image formation, cameras and lenses, object models, object manipulation, transformations, illumination, appearance modeling, and advanced rendering via ray-tracing and path-tracing. Throughout the course, students will implement a working rendering engine in a suitable programming language.

The course is meant to expose students to Web3 by covering the technical aspects of blockchain technologies and smart contracts. The course will have three learning outcomes. First, the students will learn how to build blockchains based on different consensus. Secondly, they will learn how to develop decentralized applications using smart contracts. Finally, they will learn about the current challenges of blockchain technologies such as scalability, interoperability and privacy.

This course teaches the basic techniques, methodologies, and ways of thinking underlying the application of data science and machine learning to real-world problems. Students will go through the entire process going from raw data to meaningful conclusions, including data wrangling and cleaning, data analysis and interpretation, data visualization, and the proper reporting of results. Special emphasis will be placed on ethical questions and implications in the use of AI and data. Topics include data pre-processing, web scraping, applying supervised and unsupervised machine learning methods, treating text as data, A/B testing and experimentation, and data visualization.

Public and symmetric key algorithms and their application; key management and certification; authentication protocols; digital signatures and data integrity; secure network and application protocols; application, system and network attacks and defences; intrusion detection and prevention; social engineering attacks; risk assessment and management.

Most mathematical models of real systems cannot be solved analytically and the solution of these models must be approximated by numerical algorithms. The efficiency, accuracy and reliability of numerical algorithms for several classes of models will be considered. In particular, models involving least squares, non-linear equations, optimization, quadrature, and systems of ordinary differential equations will be studied.

Implementation of database management systems. Storage management, indexing, query processing, concurrency control, transaction management. Database systems on parallel and distributed architectures. Modern database applications: data mining, data warehousing, OLAP, data on the web. Object-oriented and object-relational databases.

This course examines high-Tech innovation and entrepreneurship, principles of operation of successful high-tech enterprises, customer identification and validation, product development, business models, lean startup techniques, and financing of high-technology ventures. Students will work in teams to develop their own innovative product idea, and will produce a sound business plan to support their product.

Computer communication network principles and practice. The OSI protocol-layer model; Internet application layer and naming; transport layer and congestion avoidance; network layer and routing; link layer with local area networks, connection-oriented protocols and error detection and recovery; multimedia networking with quality of service and multicasting. Principles in the context of the working-code model implemented in the Internet.

A comprehensive introduction to the field of high-performance and parallel computing (supercomputing). Topics include foundational theory of communication and distributed systems, parallel programming, modern supercomputer hardware and architecture, and optimization and high-performance scientific computing.

The goal of this course is to examine the design and implementation of a compiler optimized for modern parallel architectures. Students will learn about common optimizations, intermediate representations (IRs), control-flow and dataflow analysis, dependence graphs, instruction scheduling, and register allocation. Advanced topics include static single assignment, memory hierarchy optimizations and parallelization, compiling for multicore machines, memory dependence analysis, automatic vectorization/thread extraction, and predicated/speculative execution.

A topic from computer science, selected by the instructor, will be covered.
The exact topic will typically change from year to year.

A topic from theoretical computer science, selected by the instructor, will be covered.
The exact topic will typically change from year to year.

A study of the theories, algorithms, and applications of Artificial Intelligence. Topics include a subset of: search, game playing, logical representations and reasoning, planning, reasoning and decision making with uncertainty, neural networks and deep learning models. Assignments provide practical experience of the core topics.