New frontiers in computing
In 2018, the Lassonde School of Engineering at York University introduced a Specialization in Artificial Intelligence (AI) in its Master of Science of Computer Science program. While AI-based research is still pursued in the general stream of the program, students in this specialization take six graduate courses, of which at least five are within the area of AI, in their first two terms. In addition, students conduct a research project that applies AI to a practical problem under the supervision of faculty members and in collaboration with partners in the private or public sector. With this knowledge, our graduates will be positioned to successfully deploy AI methodologies across many sectors.

Our Specialization in AI is officially recognized by the Vector Institute, giving our students access to the Vector Scholarships in Artificial Intelligence.
Note that the AI specialization is meant as a targeted preparation to apply AI concepts in the workplace, but being non-thesis it is not suitable to further pursue doctoral studies. Those interested in AI-based research in a thesis program should apply to the MSc in Computer Science program.

It will be the student’s responsibility to secure an internship for their research project. The Vector Institute facilitates the process by providing students in this program opportunities to interact with potential employers through networking events and the Vector Digital Talent Hub.
Below you will find a list of faculty members who are part of the Graduate Program in Electrical Engineering and Computer Science. It mentions their areas of research interests within AI (many have other research interests as well) and provides a link to their personal homepage or research group for more information about their research.
• An honours degree in Computer Science or equivalent, with at least a B+ average in the last two years of study.
• The equivalent of a senior-level course in the area of theoretical computer science.
• Minimum English language test scores (if required): TOEFL(iBT) 90, IELTS 7, or York English Language Test 4.
• The Graduate Record Examination (GRE) general test is strongly recommended, especially for applicants who did their work outside of Canada and/or the United States.
• The equivalent of a senior-level course in the area of theoretical computer science.
• Minimum English language test scores (if required): TOEFL(iBT) 90, IELTS 7, or York English Language Test 4.
• The Graduate Record Examination (GRE) general test is strongly recommended, especially for applicants who did their work outside of Canada and/or the United States.
Degree Requirements
Three courses from the following list: EECS 5326, EECS 5327, EECS 6127, EECS 6327, EECS 6412
Two other courses from the following list: EECS 5323, EECS 5324, EECS 5326, EECS 5327, EECS 6127, EECS 6322, EECS 6323, EECS 6325, EECS 6327, EECS 6328, EECS 6332, EECS 6333, EECS 6340, EECS 6390A, EECS 6390D, EECS 6412, EECS 6414
PHIL 5340
Other Requirements
• A research project that applies AI to a practical problem under the supervision of faculty members and in collaboration with partners in the private or public sector
• At least one course must be from each of the following three Groups:
Group 1: Theory of Computing & Scientific Computing (EECS 6127)
Group 2: Artificial Intelligence & Interactive Systems (EECS 5323, EECS 5324, EECS 5326, EECS 5327, EECS 6322, EECS 6323, EECS 6325, EECS 6327, EECS 6328, EECS 6332, EECS 6333, EECS 6340, EECS 6390A, EECS 6390D)
Group 3: Systems: Hardware & Software (EECS 6412, EECS 6414)
• No more than two courses can be integrated with undergraduate courses (first digit is 5)
Three courses from the following list: EECS 5326, EECS 5327, EECS 6127, EECS 6327, EECS 6412
Two other courses from the following list: EECS 5323, EECS 5324, EECS 5326, EECS 5327, EECS 6127, EECS 6322, EECS 6323, EECS 6325, EECS 6327, EECS 6328, EECS 6332, EECS 6333, EECS 6340, EECS 6390A, EECS 6390D, EECS 6412, EECS 6414
PHIL 5340
Other Requirements
• A research project that applies AI to a practical problem under the supervision of faculty members and in collaboration with partners in the private or public sector
• At least one course must be from each of the following three Groups:
Group 1: Theory of Computing & Scientific Computing (EECS 6127)
Group 2: Artificial Intelligence & Interactive Systems (EECS 5323, EECS 5324, EECS 5326, EECS 5327, EECS 6322, EECS 6323, EECS 6325, EECS 6327, EECS 6328, EECS 6332, EECS 6333, EECS 6340, EECS 6390A, EECS 6390D)
Group 3: Systems: Hardware & Software (EECS 6412, EECS 6414)
• No more than two courses can be integrated with undergraduate courses (first digit is 5)
Students admitted to the Specialization in AI are in the position to apply for institutional and government scholarships, as well as the Vector Scholarships in Artificial Intelligence, valued at $17,500 for one year. No further financial support will be provided.
CORE COURSES
Students have to complete three courses from the following list.
EECS 5326 3.0 Artificial Intelligence
This course will be an in-depth treatment of one or more specific topics within the field of artificial intelligence.
EECS 5327 3.0 Introduction to Machine Learning and Pattern Recognition
Machine learning is the study of algorithms that learn how to perform a task from prior experience. This course introduces the student to machine learning concepts and techniques applied to a pattern recognition problem in a diversity of application areas.
EECS 6127 3.0 Machine Learning Theory
This course takes a foundational perspective on machine learning and covers some of its underlying mathematical principles. Topics range from well-established results in learning theory to current research challenges. We start with introducing a formal framework, and then introduce and analyze learning methods, such as Nearest Neighbors, Boosting, SVMs and Neural Networks. Finally, students present and discuss recent research papers.
EECS 6327 3.0 Probabilistic Models & Machine Learning
Intelligent systems must make effective judgments in the face of uncertainty. This requires probabilistic models to represent complex relationships between random variables (learning) as well as algorithms that produce good estimates and decisions based on these models (inference). This course explores both probabilistic learning and inference, in a range of application areas.
EECS 6412 3.0 Data Mining
This course introduces fundamental concepts of data mining. It presents various data mining technologies, algorithms and applications. Topics include association rule mining, classification models, sequential pattern mining and clustering.
Prerequisites: an introductory course on database systems and an introductory course on probability.
ETHICS COURSE
Students have to complete the following course.
PHIL 5340 3.0 Ethics and Societal Implications of Artificial Intelligence
This course is intended for students with professional interest in the social and ethical
implications of AI. Topics include theoretical issues (could AI ever have moral rights?), practical issues (algorithmic bias, labour automation, data privacy), and professional issues (tech industry social responsibility).
OTHER AI-RELATED COURSES
Students have to complete two other courses from the following list. Only some of these courses are offered (varies year by year).
EECS 5323 3.0 Computer Vision
This course will introduce the basic concepts in Computer Vision. Primarily a survey of current computational methods, we will begin by examining methods for measuring visual data (image based operators, edge detection, feature extraction), and low-level processes for feature aggregation (optic flow, segmentation, correspondence). Finally, we will consider some issues in “high-level” vision systems.
EECS 5324 3.0 An Introduction to Robotics
This course will introduce concepts in Robotics. The course will begin with a study of the mechanics of manipulators and robot platforms. Trajectory and course planning, environmental layout and sensing will be discussed. Finally, high-level concerns will be introduced. The need for real-time response and dynamic-scene analysis will be covered, and recent developments in robotics systems from an Artificial Intelligence viewpoint will be discussed.
EECS 5326 3.0 Artificial Intelligence
This course will be an in-depth treatment of one or more specific topics within the field of artificial intelligence.
EECS 5327 3.0 Introduction to Machine Learning and Pattern Recognition
Machine learning is the study of algorithms that learn how to perform a task from prior experience. This course introduces the student to machine learning concepts and techniques applied to a pattern recognition problem in a diversity of application areas.
EECS 6127 3.0 Machine Learning Theory
This course takes a foundational perspective on machine learning and covers some of its underlying mathematical principles. Topics range from well-established results in learning theory to current research challenges. We start with introducing a formal framework, and then introduce and analyze learning methods, such as Nearest Neighbors, Boosting, SVMs and Neural Networks. Finally, students present and discuss recent research papers.
EECS 6322 3.0 Neural Networks and Deep Learning
This course covers the theory and practice of neural networks. Topics covered include training methods and loss functions, automatic differentiation and backpropagation, network architectures for a range of problems (images, text, audio, graphs, etc), validation and model selection, software tools and frameworks for deep learning.
Prerequisites: a foundational course in machine learning including, but not limited to, EECS 5327 or EECS 6327
EECS 6323 3.0 Advanced Topics in Computer Vision
An advanced topics course in computer vision which covers selected topics in greater depth. Topics covered will vary from year to year depending on the interests of the class and instructor. Possible topics include: stereo vision, visual motion, computer audition, fast image processing algorithms, vision based mobile robots and active vision sensors, and object recognition.
Prerequisites: EECS 5323.
EECS 6325 3.0 Mobile Robot Path Planning
The focus of this course is on robot motion planning in known and unknown environments. Both theoretical (computational-geometric) models, as well as practical case studies will be covered in the course.
EECS 6327 3.0 Probabilistic Models & Machine Learning
Intelligent systems must make effective judgments in the face of uncertainty. This requires probabilistic models to represent complex relationships between random variables (learning) as well as algorithms that produce good estimates and decisions based on these models (inference). This course explores both probabilistic learning and inference, in a range of application areas.
EECS 6328 3.0 Speech and Language Processing
Introducing the latest technologies in speech and language processing, including speech and recognition and understanding, keyword spotting, spoken language processing, speaker identification and verification, statistical machine translation, information retrieval, and other interesting topics.
EECS 6332 3.0 Statistical Visual Motion Analysis
A seminar course that examines statistical approaches to visual motion analysis, including 3-D structure and motion estimation, optical flow, segmentation and tracking using tools like Maximum Likelihood Estimation, Maximum A Posteriori, Least Squares and Expectation Maximization.
EECS 6333 3.0 Multiple View Image Understanding
This course considers how multiple images of a scene, as captured by multiple stationary cameras, single moving cameras or their combination, can be used to recover information about the viewed scene (e.g., three-dimensional layout, camera and/or scene movement). Theoretical and practical issues of calibration, correspondence/matching and interpretation will be considered.
Prerequisites: EECS 5323.
EECS 6340 3.0 Embodied Intelligence
This course is intended as a follow-on from a first course on Artificial Intelligence. Whereas such first courses focus on the important foundations of AI, such a Knowledge Representation or Reasoning, this course will examine how these separate foundational elements can be integrated into real systems. This will be accomplished by detailing some general overall concepts that form the basis of intelligent systems in the real world, and then presenting a number of in-depth cases studies of a variety of systems from several applications domains. The embodiment of intelligence may be in a physical system (such as a robot) or a software system (such as in game-playing) but in both cases, the goal is to interact with, and solve a problem in, the real world.
Prerequisites: introductory courses in artificial intelligence. robotics, and computer vision.
EECS 6390A 3.0 Knowledge Representation
This course examines some of the techniques used to represent knowledge in artificial intelligence, and the associated methods of automated reasoning. The emphasis will be on the compromises involved in providing a useful but tractable representation and reasoning service to a knowledge-based system. The topics may include: formal models of knowledge and belief, systems of limited reasoning, languages of limited expressive power, defaults and exceptions, meta-level representation and reasoning, reasoning about action, and theories of rational agency.
Prerequisites: an introductory course on first-order logic.
EECS 6390D 3.0 Computational Models of Visual Perception
This course examines the problem of developing rigorous computational models for visual processing. Computational strategies may draw upon techniques in statistical inference, signal processing, optimization theory, graph theory and distributed computation.
EECS 6412 3.0 Data Mining
This course introduces fundamental concepts of data mining. It presents various data mining technologies, algorithms and applications. Topics include association rule mining, classification models, sequential pattern mining and clustering.
Prerequisites: an introductory course on database systems and an introductory course on probability.
EECS 6414 3.0 Data Analytics and Visualization
Data analytics and visualization is an emerging discipline of immense importance to any data-driven organization. This is a project-focused course that provides students with knowledge on tools for data mining and visualization and practical experience working with data mining and machine learning algorithms for analysis of very large amounts of data. It also focuses on methods and models for efficient communication of data results through data visualization.
Prerequisites: introductory courses in algorithms, probability theory, linear algebra, and programming.

AI Specialization Faculty
Robert Allison
Vision and intelligent interfaces
Aijun An
Website
Data mining, machine learning, information retrieval, and AI
Michael Brown
Computer vision and AI
Natalija Vlajic
Machine learning in computer security
Marcus Brubaker
Machine learning, probabilistic methods, computer vision and computational biology
Suprakash Datta
Machine learning for bioinformatics
Kosta Derpanis
Computer vision and machine learning
James Elder
AI and vision
Petros Faloutsos
AI for computer games and virtual humans
Gerd Grau
AI-based materials and process development
Michael Jenkin
Robotics and AI
Hui Jiang
Machine learning, speech and language processing, and computer vision
Ingo Fruend
Human vision and AI
Ali Hooshyar
Smart grid analysis using AI
Zhen Ming (Jack) Jiang
Software analytics and software performance engineering
Richard Wildes
AI and vision
Hossein Kassiri
AI-based algorithms for decoding a physiological/neurological function
Matthew Kyan
Machine intelligence approach for virtual environments
Yves Lesperance
Knowledge representation and reasoning, autonomous agents and multi-agent systems, and cognitive robotics
Peter Lian
Circuits and systems for embedded AI and neuromorphic computing
Marin Litoiu
Adaptive software and autonomic computing
Sebastian Magierowski
Hardware acceleration for machine learning
Manos Papagelis
Data mining, graph mining, machine learning, big data analytics, knowledge discovery
Ali Sadeghi-Naini
no link to personal webpage yet
AI in precision medicine; Machine learning in image-guided therapeutics; Quantitative imaging and radiomics
Mikhail Soutchanski
AI planning, planning in hybrid systems, knowledge representation including causality, reinforcement learning for planning
Pirathayini Srikantha
Game theory, large-scale optimization and distributed control for enabling adaptive, sustainable and resilient power grid operations
Zbigniew Stachniak
Automated reasoning and propositional satisfiability
Vassilios Tzerpos
Machine learning and audio
Ruth Urner
Machine learning theory
Franck van Breugel
Reinforcement learning for finding bugs in software