Computer Science - Artificial Intelligence Major (B.Sc.) (63 credits)
Offered by: Computer Science (Faculty of Science)
Degree: Bachelor of Science
Program credit weight: 63
Program Description
The B.Sc.; Major in Computer Science: Artificial Intelligence focuses on topics that relate to artificial intelligence and machine learning, including both foundations and applications. Students may complete this program with a minimum of 63 credits or a maximum of 68 credits.
Degree Requirements — B.Sc.
This program is offered as part of a Bachelor of Science (B.Sc.) degree.
To graduate, students must satisfy both their program requirements and their degree requirements.
- The program requirements (i.e., the specific courses that make up this program) are listed under the Course Tab (above).
- The degree requirements—including the mandatory Foundation program, appropriate degree structure, and any additional components—are outlined on the Degree Requirements page.
Students are responsible for ensuring that this program fits within the overall structure of their degree and that all degree requirements are met. Consult the Degree Planning Guide on the SOUSA website for additional guidance.
Note: For information about Fall 2025 and Winter 2026 course offerings, please check back on May 8, 2025. Until then, the "Terms offered" field will appear blank for most courses while the class schedule is being finalized.
Required Courses (39-42 credits)
| Course | Title | Credits |
|---|---|---|
| COMP 202 | Foundations of Programming. 1 | 3 |
Foundations of Programming. Terms offered: Summer 2025 Introduction to computer programming in a high level language: variables, expressions, primitive types, methods, conditionals, loops. Introduction to algorithms, data structures (arrays, strings), modular software design, libraries, file input/output, debugging, exception handling. Selected topics. | ||
| COMP 206 | Introduction to Software Systems. | 3 |
Introduction to Software Systems. Terms offered: this course is not currently offered. Comprehensive overview of programming in C, use of system calls and libraries, debugging and testing of code; use of developmental tools like make, version control systems. | ||
| COMP 250 | Introduction to Computer Science. | 3 |
Introduction to Computer Science. Terms offered: this course is not currently offered. Mathematical tools (binary numbers, induction,recurrence relations, asymptotic complexity,establishing correctness of programs). Datastructures (arrays, stacks, queues, linked lists,trees, binary trees, binary search trees, heaps,hash tables). Recursive and non-recursivealgorithms (searching and sorting, tree andgraph traversal). Abstract data types. Objectoriented programming in Java (classes andobjects, interfaces, inheritance). Selected topics. | ||
| COMP 251 | Algorithms and Data Structures. | 3 |
Algorithms and Data Structures. Terms offered: this course is not currently offered. Data Structures: priority queues, balanced binary search trees, hash tables, graphs. Algorithms: topological sort, connected components, shortest paths, minimum spanning trees, bipartite matching, network flows. Algorithm design: greedy, divide and conquer, dynamic programming, randomization. Mathematicaltools: proofs of asymptotic complexity and program correctness, Master theorem. | ||
| COMP 273 | Introduction to Computer Systems. | 3 |
Introduction to Computer Systems. Terms offered: this course is not currently offered. Number representations, combinational and sequential digital circuits, MIPS instructions and architecture datapath and control, caches, virtual memory, interrupts and exceptions, pipelining. | ||
| COMP 302 | Programming Languages and Paradigms. | 3 |
Programming Languages and Paradigms. Terms offered: this course is not currently offered. Programming language design issues and programming paradigms. Binding and scoping, parameter passing, lambda abstraction, data abstraction, type checking. Functional and logic programming. | ||
| COMP 303 | Software Design. | 3 |
Software Design. Terms offered: this course is not currently offered. Principles, mechanisms, techniques, and tools for object-oriented software design and its implementation, including encapsulation, design patterns, and unit testing. | ||
| COMP 424 | Artificial Intelligence. | 3 |
Artificial Intelligence. Terms offered: this course is not currently offered. Introduction to search methods. Knowledge representation using logic and probability. Planning and decision making under uncertainty. Introduction to machine learning. | ||
| MATH 222 | Calculus 3. | 3 |
Calculus 3. Terms offered: Summer 2025 Taylor series, Taylor's theorem in one and several variables. Review of vector geometry. Partial differentiation, directional derivative. Extreme of functions of 2 or 3 variables. Parametric curves and arc length. Polar and spherical coordinates. Multiple integrals. | ||
| MATH 223 | Linear Algebra. | 3 |
Linear Algebra. Terms offered: this course is not currently offered. Review of matrix algebra, determinants and systems of linear equations. Vector spaces, linear operators and their matrix representations, orthogonality. Eigenvalues and eigenvectors, diagonalization of Hermitian matrices. Applications. | ||
| MATH 240 | Discrete Structures. | 3 |
Discrete Structures. Terms offered: this course is not currently offered. Introduction to discrete mathematics and applications. Logical reasoning and methods of proof. Elementary number theory and cryptography: prime numbers, modular equations, RSA encryption. Combinatorics: basic enumeration, combinatorial methods, recurrence equations. Graph theory: trees, cycles, planar graphs. | ||
| MATH 323 | Probability. | 3 |
Probability. Terms offered: Summer 2025 Sample space, events, conditional probability, independence of events, Bayes' Theorem. Basic combinatorial probability, random variables, discrete and continuous univariate and multivariate distributions. Independence of random variables. Inequalities, weak law of large numbers, central limit theorem. | ||
| MATH 324 | Statistics. | 3 |
Statistics. Terms offered: this course is not currently offered. Sampling distributions, point and interval estimation, hypothesis testing, analysis of variance, contingency tables, nonparametric inference, regression, Bayesian inference. | ||
- 1
Students who have sufficient knowledge in a programming language do not need to take COMP 202 Foundations of Programming..
Complementary Courses (24-26 credits)
Group A
6 credits selected from:
| Course | Title | Credits |
|---|---|---|
| COMP 330 | Theory of Computation. | 3 |
Theory of Computation. Terms offered: this course is not currently offered. Finite automata, regular languages, context-free languages, push-down automata, models of computation, computability theory, undecidability, reduction techniques. | ||
| COMP 350 | Numerical Computing. | 3 |
Numerical Computing. Terms offered: this course is not currently offered. Computer representation of numbers, IEEE Standard for Floating Point Representation, computer arithmetic and rounding errors. Numerical stability. Matrix computations and software systems. Polynomial interpolation. Least-squares approximation. Iterative methods for solving a nonlinear equation. Discretization methods for integration and differential equations. | ||
| COMP 360 | Algorithm Design. | 3 |
Algorithm Design. Terms offered: this course is not currently offered. Advanced algorithm design and analysis. Linear programming, complexity and NP-completeness, advanced algorithmic techniques. | ||
Group B
3 credits selected from:
| Course | Title | Credits |
|---|---|---|
| COMP 310 | Operating Systems. | 3 |
Operating Systems. Terms offered: this course is not currently offered. Control and scheduling of large information processing systems. Operating system software - resource allocation, dispatching, processors, access methods, job control languages, main storage management. Batch processing, multiprogramming, multiprocessing, time sharing. | ||
| COMP 421 | Database Systems. | 3 |
Database Systems. Terms offered: this course is not currently offered. Database Design: conceptual design of databases (e.g., entity-relationship model), relational data model, functional dependencies. Database Manipulation: relational algebra, SQL, database application programming, triggers, access control. Database Implementation: transactions, concurrency control, recovery, query execution and query optimization. | ||
Group C
3 or 4 credits selected from:
| Course | Title | Credits |
|---|---|---|
| COMP 451 | Fundamentals of Machine Learning. | 3 |
Fundamentals of Machine Learning. Terms offered: this course is not currently offered. Introduction to the computational, statistical and mathematical foundations of machine learning. Algorithms for both supervised learning and unsupervised learning. Maximum likelihood estimation, neural networks, and regularization. | ||
| COMP 551 | Applied Machine Learning. | 4 |
Applied Machine Learning. Terms offered: this course is not currently offered. Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems. | ||
Group D
3 credits selected from:
| Course | Title | Credits |
|---|---|---|
| COMP 345 | From Natural Language to Data Science. | 3 |
From Natural Language to Data Science. Terms offered: this course is not currently offered. Introduction to language data science, including theoretical approaches and practical skills. Processing, searching, and querying text data; making sense of large corpora; modelling and interpreting psycholinguistic and historical language data; building models of sequences of words; computing similarity between languages; information retrieval and extraction; question answering; and ethics. | ||
| COMP 370 | Introduction to Data Science. | 3 |
Introduction to Data Science. Terms offered: this course is not currently offered. Comprehensive introduction to the data science process. Orientation to the use and configuration of core data science toolkits, data collection and annotation fundamentals, principles of responsible data science, the use of quantitative tools in data science, and presentation of data science findings. | ||
Group E
3 or 4 credits selected from:
| Course | Title | Credits |
|---|---|---|
| COMP 417 | Introduction Robotics and Intelligent Systems. | 3 |
Introduction Robotics and Intelligent Systems. Terms offered: this course is not currently offered. This course considers issues relevant to the design of robotic and of intelligent systems. How can robots move and interact. Robotic hardware systems. Kinematics and inverse kinematics. Sensors, sensor data interpretation and sensor fusion. Path planning. Configuration spaces. Position estimation. Intelligent systems. Spatial mapping. Multi-agent systems. Applications. | ||
| COMP 445 | Computational Linguistics. | 3 |
Computational Linguistics. Terms offered: this course is not currently offered. Introduction to foundational ideas in computational linguistics and natural language processing. Topics include formal language theory, probability theory, estimation and inference, and recursively defined models of language structure. Emphasis on both the mathematical foundations of the field as well as how to use these tools to understand human language. | ||
| COMP 511 | Network Science. | 4 |
Network Science. Terms offered: this course is not currently offered. Selected topics in Network Science, Graph Mining and Graph Learning, including patterns in real world networks, ranking and similarity measures for graphs, graph clustering and community mining techniques, and node classification and link prediction methods. | ||
| COMP 514 | Applied Robotics. | 4 |
Applied Robotics. Terms offered: this course is not currently offered. The approach and the challenges in the key components of manipulators and locomotors: representations, kinematics, dynamics, rigid-body chains, redundant systems, under-actuated systems, control, planning, and perception. Practical aspects of robotics: collisions, integrating sensory feedback, and real-time software development. | ||
| COMP 545 | Natural Language Understanding with Deep Learning . | 4 |
Natural Language Understanding with Deep Learning . Terms offered: this course is not currently offered. Neural network-based methods for natural language understanding (NLU) and computational semantics. Continuous representations for words, phrases, sentences, and discourse, and their connection to formal semantics. Practical and ethical considerations in applications such as text classification, question answering, and conversational assistants. | ||
| COMP 549 | Brain-Inspired Artificial Intelligence. | 3 |
Brain-Inspired Artificial Intelligence. Terms offered: this course is not currently offered. Overview of the influence of neuroscience and psychology on Artificial Intelligence (AI). Historical topics: perceptrons, the PDP framework, Hopfield nets, Boltzmann and Helmholtz machines, and the behaviourist origins of reinforcement learning. Modern topics: deep learning, attention, memory and consciousness. Emphasis on understanding the interdisciplinary foundations of modern AI. | ||
| COMP 550 | Natural Language Processing. | 3 |
Natural Language Processing. Terms offered: this course is not currently offered. An introduction to the computational modelling of natural language, including algorithms, formalisms, and applications. Computational morphology, language modelling, syntactic parsing, lexical and compositional semantics, and discourse analysis. Selected applications such as automatic summarization, machine translation, and speech processing. Machine learning techniques for natural language processing. | ||
| COMP 558 | Fundamentals of Computer Vision. | 4 |
Fundamentals of Computer Vision. Terms offered: this course is not currently offered. Image filtering, edge detection, image features and histograms, image segmentation, image motion and tracking, projective geometry, camera calibration, homographies, epipolar geometry and stereo, point clouds and 3D registration. Applications in computer graphics and robotics. | ||
| COMP 562 | Theory of Machine Learning. | 4 |
Theory of Machine Learning. Terms offered: this course is not currently offered. Concentration inequalities, PAC model, VC dimension, Rademacher complexity, convex optimization, gradient descent, boosting, kernels, support vector machines, regression and learning bounds. Further topics selected from: Gaussian processes, online learning, regret bounds, basic neural network theory. | ||
| COMP 565 | Machine Learning in Genomics and Healthcare. | 4 |
Machine Learning in Genomics and Healthcare. Terms offered: this course is not currently offered. Linear models in statistical genetics, causal inference, single-cell genomics, multi-omic learning, electronic health record mining. Applications of machine learning techniques: linear regression, latent factor models, variational Bayesian inference, neural networks, model interpretation. | ||
| COMP 579 | Reinforcement Learning. | 4 |
Reinforcement Learning. Terms offered: this course is not currently offered. Bandit algorithms, finite Markov decision processes, dynamic programming, Monte-Carlo Methods, temporal-difference learning, bootstrapping, planning, approximation methods, on versus off policy learning, policy gradient methods temporal abstraction and inverse reinforcement learning. | ||
| COMP 585 | Intelligent Software Systems . | 4 |
Intelligent Software Systems . Terms offered: this course is not currently offered. Practical aspects of building software systems with machine learning components: requirements, design, delivery, quality assessment, and collaboration. Consideration of a user-centered mindset in development; integration of design and development considerations relevant to artificial intelligence, such as security, privacy, and fairness. | ||
| ECSE 552 | Deep Learning. | 4 |
Deep Learning. Terms offered: this course is not currently offered. Overview of mathematical background and basics of machine learning, deep feedforward networks, regularization for deep learning, optimization for training deep learning models, convolutional neural networks, recurrent and recursive neural networks, practical considerations,applications of deep learning, recent models and architectures in deep learning. | ||
| ECSE 557 | Introduction to Ethics of Intelligent Systems. | 3 |
Introduction to Ethics of Intelligent Systems. Terms offered: this course is not currently offered. Ethics and social issues related to AI and robotic systems. Consideration for normative values (e.g., fairness) in the design. Ethics principles, data and privacy issues, ethics challenges in interaction and interface design. | ||
Group F
6 credits of COMP courses at the 300 level or above (except COMP 396 Undergraduate Research Project.).