Computer Science Department

This course introduces coding for everyday problem solving. Programming fundamentals are introduced with an intuitive programming language and a simple programming environment. The students will obtain first-hand experience with live coding examples and exercises. Students from all disciplines can learn to develop their programming abilities without any prior knowledge. For computer science major, advanced major and honours students CSCI 128 may be available only as an approved or open elective. Students who have received credit for CSCI 161 or equivalent are not permitted to enrol in CSCI 128. Three credits.

This course enables students to use a variety of software tools to assist in their post-secondary studies and future careers. The course covers a broad range of information and communication tools essential for analyzing and presenting data, communicating information, organizing and writing papers, and preparing talks, slide presentations and posters. Webpage management is introduced. Topics covered support students in education, business, humanities and the health/social/physical sciences. For computer science major, advanced major and honours students CSCI 135 may be available only as an approved or open elective. Credit will be granted for only of CSCI 135 or CSCI 235. Three credits.

An introduction to computers, algorithms and programming. Topics include problem analysis, algorithm development, data representation, control structures, arrays, and file manipulation. Three credits and a two-hour lab.

Continuing from the material in CSCI 161, this course covers memory management and data abstraction via classes and objects, and introduces the linear data structures lists, stacks, and queues. Structured programming is encouraged via modular development. Prerequisite: CSCI 161. Three credits and a two-hour lab.

This course exposes students to the various impacts of technology on modern society with the goal of further developing their critical thinking and their ability to make informed decisions in this rapidly changing information age. Topics covered include privacy and security, biotechnology, cybercrime, genetic engineering, artificial intelligence, digitization and intellectual property, ethical issues in computing. Other topics and/or their emphasis may vary by semester. Students from every background will benefit from this course. Three credits.

The course will provide students with the basic understanding of the theory and practice of data science and its applications in different real-world domains. Student will also gain practical skills in handling structured and unstructured data, analyzing and visualizing data, data mining, as well as gain hands-on experience of software tools and apply the basic techniques to their own different scientific, engineering and business applications. Prerequisite: One of CSCI 125, 128, 161 or 225. Three credits.

Technological development has transformed modern healthcare. The large amounts of health data currently acquired and analyzed has the potential to positively affect a patient’s quality of life. This interdisciplinary course focuses on developing practical coding skills used in the healthcare domain, a rapidly growing field of computing that can have a beneficial impact on patient care and public health. Suitable for students from a variety of backgrounds planning a career involving health-related data. Open to students in all degree programs. Prerequisite: CSCI 128 or 161 or with permission of department chair. Three credits.

This course provides a deep investigation of foundational data structures and algorithms. Criteria for selecting appropriate data structures and algorithms for a given problem are presented. General problem solving is emphasized throughout the course. Specific topics include stacks, queues, lists, trees, searching, sorting, traversals, recursion, graphs, hashing, and complexity analysis. Prerequisite: CSCI 162. Three credits and a two-hour lab.

This course covers basic computer arithmetic, architectures, and instruction sets; in-depth study of the central processing unit, memory and input/output organization; and microprogramming and interfacing. Credit will be granted for only one of CSCI 263 or INFO 225. Prerequisite: CSCI 162. Three credits and a two-hour lab.

An introduction to the theory and practice associated with the design and implementation of databases. Topics include database models (relational model in detail), design, normalization, transactions, SQL, and a DBMS (Oracle). Credit will be granted for only one of CSCI 275, BSAD 384 or INFO 275. Prerequisite: CSCI 162. Three credits.

An introduction to sets, binary relations and operations; induction and recursion; partially ordered sets; simple combinations; truth tables; Boolean algebras and elementary group theory, with applications to logic networks, trees and languages; binary coding theory and finite-state machines. Cross-listed as MATH 277. Prerequisites: MATH 101, 102 or 107 or 127 or 122 or CSCI 162. Three credits.

Evolutionary computation is a family of powerful optimization algorithms often used to find solutions to computationally intractable problems. The study of these algorithms and their application to problems is a large research area within computer science. Course topics include combinatorial optimization, genetic algorithms, particle swarm optimization, search space analysis, multi-objective optimization, and neuroevolution. Research practices and technical writing will be emphasized for course assignments/projects. Prerequisites: CSCI 255, CSCI 223 or 275; or permission of chair. Three credits.

An introduction to the design, analysis, and implementation of algorithms to solve common computational problems. Basic algorithm design techniques such as the greedy strategy, divide-and-conquer, and dynamic programming, as well as network flows, intractability, and NP-completeness will be discussed. Prerequisites: CSCI 255, 277. Three credits and a two-hour lab.

An introduction to the theoretical foundations of computer science, examining finite automata, context-free grammars, Turing machines, decidability and undecidability, and complexity theory. Strategies will be developed to help categorize problems as tractable or intractable. Prerequisites: CSCI 255, 277. Three credits.

This course covers communication systems; environments and components; common carrier services; network control, design and management; distributed and local networks. Credit will be granted for only one of CSCI 368 or INFO 465. Prerequisite: CSCI 255. Three credits and a two-hour lab.

This course explores current topics in computer science, such as big data, distributed computing, bioinformatics and machine learning. Three credits.

An overview of operating systems functions: file management, CPU scheduling, process management, synchronization, memory management, and deadlock handling. UNIX will be introduced and used in this course. Prerequisite: CSCI 263, completed or concurrent. Three credits and a two-hour lab.

A survey of advanced topics in algorithms and complexity theory. Topics include online, randomized, approximation, and parameterized algorithms as well as other advanced algorithm design techniques, average-case analysis, and amortized analysis. Prerequisite: CSCI 355 or permission of the chair. Three credits. Offered 2024-2025 and in alternate years.

This course covers modern technologies in computational machine learning. Validation of machine learning algorithms will be taught alongside computational design considerations for the creation of reliable and robust machine learning models. Machine learning techniques will be taught in detail from a computational technology perspective, including decision trees, bootstrapping, bagging, super learners, AdaBoost, artificial & convolutional neural networks and methods for minimizing error on unseen data. Classical learning techniques will also be presented. Prerequisites: CSCI 161, STAT 224 or 231 or 101 or permission of department chair. Three credits.

Covers the theory and practice of computer and network security, including cryptography, authentication, network security, and computer system security. Topics include secret and public key cryptography; message digests; authentication, including password-based, address-based, and cryptographic; network security; system security, including intruders, malicious software, and firewalls. Students will use and implement algorithms. Prerequisite: CSCI 368, completed or concurrent. Three credits. Offered 2024-2025 and in alternate years.

The course covers techniques for the design and management of large software projects, including structured programming, debugging, and testing methodologies. Examples of large systems will be provided and a programming project will be completed. Prerequisite: CSCI 162; 483 is recommended. Three credits.

Students will prepare and present a thesis based on original research conducted under the supervision of a faculty member. Credit will be granted for only one of CSCI 490 or CSCI 493. Restricted to students in the honours program. Required for honours students. Six credits.

The purpose of this non-credit course is to assist students in carrying out research, composition, and oral presentation. Students will present a project topic in the fall term and their project in the spring. Attendance at departmental seminars is mandatory. No credit.

493 Senior Thesis
Students will prepare and present a thesis based on original research conducted under the supervision of a faculty member. Required for honours students; permitted for advanced major students. Three credits.

An introduction to the core concepts of artificial intelligence, including state space, heuristic search techniques, knowledge representation, logical inference, uncertain reasoning, and machine learning. Specific methods covered include neural networks, genetic algorithms, and reinforcement learning. Prerequisites: CSCI 255, 263, 277. Three credits.

This course covers analysis techniques and development methodology for real-time systems. Topics include real-time process and control, soft and hard real time systems, real-time scheduling algorithms, schedulability analysis theory, resource access control, real-time operating systems, real-time communications, performance analysis, requirement specification and system specification, verification of real-time systems, and formal development process of time critical real-time systems. Three credits.

This course covers modern technologies in computational machine learning with advanced applications in deep learning. Validation of machine learning algorithms will be taught alongside computational design considerations for the creation of reliable and robust machine learning models. Technologies taught will include autoencoders, deep learning for segmentation (U-Nets etc.), recurrent neural networks, long short-term memory learning machines and explainable artificial intelligence. Classical machine learning techniques will also be presented for breadth of background. Three credits.

The emphasis of this course is to introduce big data concepts. Course topics include reliable and big data storage, efficient big data processing and analytics. Students will gain abilities to design highly scalable systems that can store, process, and analyze a big volume of unstructured and/or semi-structured data in batch mode and/or real time. Three credits.

The course will introduce advanced algorithms for structured data analytics and their applications in real-world problems. Course topics include classification, cluster analysis, association analysis, and anomaly detection. Students will learn these algorithms with hands-on implementation and gain abilities to derive value from collected data by applying the advanced data analytics algorithms. Three credits.

This course will introduce basic concepts and techniques used within the field of mobile robotics. Classical motion planning algorithms, such as A* and RRT will be taught. During this course, machine learning models related to robotics will also be taught. The fundamental challenges for autonomous intelligent systems will be analyzed and an approximation method to calculate a solution will be discussed. The concepts taught will include Bayesian filters, Kinematics, Sensors, Markov Decision Process, POMDP and Reinforcement Learning. Three credits.

This course will introduce the fundamentals of Reinforcement learning and Deep learning techniques. The course will cover the Tabular solution methods, such as the finite Markov Decision Processes and Temporal-Difference learning. It will also cover approximation solution methods, as on-policy and off-policy approximations. By the end of the course, new deep-learning techniques will be introduced. Strong fundamentals in calculus and data structure are recommended. Restricted to MACS and MSc-CSCI students. Three credits.

This course presents students with methods to automatically analyze text written in a natural language and considers ethical concerns on biased data. It explores modern techniques such as embedding-based and neural models. This course presents approaches and their applicability across different tasks, such as, sentiment analysis, language modeling, and document classification. Students are expected to code solutions for assignments. Three credits.

An advanced course building on foundational ideas in the theory of computing. Further properties of regular and context-free languages, language classes beyond context-free, parsing, randomness and probabilistic computation, relativized computation, complexity hierarchies, and circuit complexity will be discussed. Prior experience with theory of computing at the undergraduate level is recommended. Three credits.

This course provides a survey of general methods for analyzing knowledge about the real world and mapping it to a computable form. Principles of knowledge representation and their role in adapting logic and ontology to the task of constructing computable models of an application domain are introduced. Methods for representing dynamically changing processes and events are presented. Ways of dealing with vague, uncertain, imprecise or inconsistent facts are discussed. Three credits.

This course focuses on automated theorem proving. We start with a rigorous treatment of propositional and first order calculus (with equality) and the method of natural deduction, giving a thorough investigation of the soundness and completeness proofs and decidability. Then we compare and contrast several automated theorem proving methods such as tableau, resolution, sequent style calculus and rewrite systems. Extensions to other logics will be discussed. Students will implement one of the automated theorem proving methods. Three credits.

Through the use of lectures, discussions, the text, assignments, and labs, this course will familiarize students with the advanced knowledge of triangular systems, positive definite systems, banded systems, sparse positive definite systems, general systems; Sensitivity of linear systems; orthogonal matrices and least squares; singular value decomposition; eigenvalues and eigenvectors; and QR algorithm with their applications. Three credits.

The course covers the most current techniques used in data mining and machine learning and their background theoretical results. Two basic groups of methods are covered in this course: supervised learning (classification or regression) and unsupervised learning (clustering). The supervised learning methods includes Recursive Partitioning Tree, Random Forest, Linear Discriminant and Quadratic Discriminant Analysis, Neural Network, Support Vector Machine. The unsupervised learning methods include Hierarchical Clustering, K-means, K-nearest-neighbour, model-based clustering methods. Furthermore, the course also covers the dimensional reduction techniques such as LASSO and Ridge Regression, and model checking criteria. Three credits.

The objective of the course is to provide a broad overview of issues and approaches, while exposing students to recent advancements in computer and network security. This course will cover the theory and practice of computer and network security. While covering the theory of computer communication security, the course will focus on using and in some cases implementing various algorithms as well. Three credits.

The course will examine combinatorial problem solving and optimization with constraint processing and heuristic search methods for a variety of real world applications. It contains two main parts. The first part covers basic and advanced search techniques and the second part studies constraint processing techniques and constraint programming. Three credits.

This seminar course prepares graduates for industry or academia by developing knowledge and skills that will be applicable in a variety of professional contexts. Among these skills will be professional communication with industry and non-industry audiences, social and ethical issues in the field, grant and proposal writing, job search skills, research skills, and current innovations in research. The course is facilitated by computer science faculty members and includes presentations by invited experts. Pass/Fail. Six credits.