Automata, Computability and ComplexityIntroduction to more advanced topics in theoretical foundations of computer science, including the study of formal languages and automata, formal models of computation, and computational complexity measures.
PSEPetrie Science & Engineering Building321
Artificial IntelligenceThis course will be an in-depth treatment of one or more specific topics within the field of Artificial Intelligence.
LSBLife Science Building101
Operating Systems DesignA modern operating system has four major components: process management, input/output, memory management, and the file system. This project-oriented course puts operating system principals into action and presents a practical approach to studying implementation aspects of operating systems. A series of projects are included for students to acquire direct experience in the design and construction of operating system components and have each interact correctly with the existing software. The programming environment is C/C++ under UNIX.
Computer ArchitectureThis course presents the core concepts of computer architecture and design ideas embodied in many machines and emphasizes a quantitative approach to cost/performance tradeoffs. This course concentratres on uniprocessor systems. A few machines are studies to illustrate how these concepts are implemented; how various tradeoffs that exit among design choices are treated; and how good designs make efficient use of technology. Future trends in computer architecture are also discussed.
Distributed ComputingCan a given problem be solved in a distributed system? If so, how efficiently can it be solved? We investigate how the answers to these questions depend on aspects of the underlying distributed system including synchrony, fault-tolerance and the means of communication between processes.
Principles of Human Perception and PerformanceThis course considers the role of human perception in human-computer interaction particularly computer generated graphics/sound and immersive virtual reality. Fundamental findings from sensory physiology and perceptual psychophysics are presented in the context of interface and display design.
BSBBehavioural Science Building207
Probabilistic Models & Machine LearningIntelligent systems must make effective judgements 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.
Adaptive Software SystemsAdaptive software systems are software systems that change their behaviour and structure to cope with changes in environment conditions or in user requirements. Adaptation includes self-optimization, self-protection, self-configuration and self-healing. This course covers basic and advanced concepts in engineering adaptive systems and has a special focus on self-optimization. It introduces the students to the mathematical foundations of adaptive systems including performance models, estimators for performance models, feedback loop architectures and strategies, and optimization.
High Frequency Power Electronic ConvertersThis course discusses the fundamentals of loss-less switching techniques in high frequency power converters: zero-voltage switching and zero-current switching. The course then focuses on various resonant converter topologies and soft-switching converters with auxiliary storage elements. The course then discusses various control techniques used in high frequency power converters. Special emphasis is placed on the design techniques using practical examples.
Advanced Data StructuresThe course discusses advanced data structures: heaps, balanced binary search trees, hashing tables, red--black trees, B--trees and their variants, structures for disjoint sets, binomial heaps, Fibonacci heaps, finger trees, persistent data structures, etc. When feasible, a mathematical analysis of these structures will be presented, with an emphasis on average case analysis and amortized analysis. If time permits, some lower bound techniques may be discussed, as well as NP-completeness proof techniques and approximation algorithms.
Introduction to RoboticsThis course introduces concepts in Robotics. The course begins with a study of the mechanics of manipulators and robot platforms. Trajectory and course planning, environmental layout and sensing are discussed. Finally, high-level concerns are introduced. The need for real-time response and dynamic-scene analysis are covered, and recent development in robotics systems from an Artificial Intelligence viewpoint are discussed.
Introduction to Machine Learning and Pattern RecognitionMachine 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 pattern recognition problem in a diversity of application areas.
Advanced Topics in 3D Computer GraphicsThis course introduces advanced 3D computer graphics algorithms. Topics may include direct programming of graphics hardware via pixel and vertex shaders, real-time rendering, global illumination algorithms, advanced texture mapping and anti-aliasing, data visualization.
Mobile CommunicationThis course provides an overview of the latest technology, developments and trends in wireless mobile communications, and addresses the impact of wireless transmission and user mobility on the design and management of wireless mobile systems.
Real-Time Systems PracticeIntroduction to the technologies related to the design and implementation of real-time systems. State-of-the-art real-time system technologies and their use in typical real-time applications are studied in detail.
DBVictor Phillip Dahdaleh Building0015
Digital Very Large Scale IntegrationA course on modern aspects of VLSI CMOS chips. Key elements of complex digital system design are presented including design automation, nanoscale MOS fundamentals, CMOS combinational and sequential logic design, datapath and control system design, memories, testing, packaging, I/O, scalability, reliability, and IC design economics.
Advanced Algorithm Design and AnalysisThis is an advanced theoretical computer science course directed at non-theory students with the standard undergraduate background. The goal is to survey the key theory topics that every computer science graduate student should know. In about two weeks for each selected topic, we will gain insights into the basics and study one or two example in depth. These might include: a deepening of student's knowledge of key algorithmic techniques, randomized algorithms, NPcompleteness, approximation algorithms, linear programming, distributed systems, computability, concurrency theory, cryptography, structural complexity, data structures, and quantum algorithms. Students will be expected to give a presentation on some topic new to them and solve some difficult problems in homework assignments.
PSEPetrie Science and Engineering Building317
Advanced Topics in Computer VisionAn 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.
Embodied IntelligenceThis 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.
BSBBehavioural Science Building207
Data MiningThis 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.
Information NetworksThe field of information networks is an emerging discipline of immense importance that combines graph theory, probability and statistics, data analysis, and computational social science. This course provides students with both theoretical knowledge and practical experience of the field by covering recent research on models and algorithms of information networks and their basic properties.
SCStong College 219
Advanced Data SystemsThis course provides an introduction to, and an in-depth study on, several new developments in database systems and intelligent information systems. Topics include: internet databases, data warehousing and OLAP, object-relational, object-oriented, and deductive databases.
Software Re-EngineeringIndustrial software systems are usually large and complex, while knowledge of their structure is either lost or inadequately documented. This course presents techniques that aid the comprehension and design recovery of large software systems.
Micro-fluidics for Cellular and Molecular BiologyThis course offers an introduction to the micro-fluidics for life science applications. This course offers a unique opportunity to all science, health and engineering students to learn the fundamental of micro-fluidic technologies for a variety of cellular and molecular applications. The coverage is both practical and in depth integrating experimental, theoretical and simulation examples.
Students are required to complete five graduate courses. Of those five courses, at most one course can be an integrated course (the first digit of the course is a 5) and at most one course can be a directed reading course (see below). Students must take at least one course of group 1, at least one course of group 2, and at least one course of group 3. Full-time students are recommended to take three courses in the fall and two courses in the winter. Full-time students are required to complete their courses within the first two terms. Students are encouraged to discuss their course selection with their supervisor or the graduate program director. Students must maintain a B+ average in their courses.
Students are required to complete seven graduate courses. Of those seven courses, at most two courses can be an integrated course (the first digit of the course is a 5) and at most one course can be a directed reading course (see below). Students must take at least one course of group 1, at least one course of group 2, and at least one course of group 3. Full-time students are recommended to take three courses in the fall, three courses in the winter, and a directed reading course in the summer. Full-time students are required to complete their courses within the first four terms. Students are encouraged to discuss their course selection with their supervisor or the graduate program director. Students must maintain a B+ average in their courses.
Students are required to complete the research project course (see below) and three other courses. Of those three courses, at most one course can be an integrated course (the first digit of the course is a 5) and at most one course can be a directed reading course (see below). Students must take
Full-time students are recommended to take the research project course in the fall and the winter, two other courses in the fall, and one other course in the winter. Full-time students are required to complete their courses within the first two terms. Students are encouraged to discuss their course selection with their supervisor or the graduate program director. Students must maintain a B+ average in their courses.
Students are required to complete three graduate courses. Of those three courses, at most one course can be an integrated course (the first digit of the course is a 5) and at most one course can be a directed reading course (see below). Note that due to the prerequisite structure, students may have to complete more than three courses. Students are recommended to take three courses in the fall. Students are required to complete their courses within the first three terms. Students are encouraged to discuss their course selection with their supervisor. Students must obtain a B+ average in their courses.
A directed reading course is suited for students with special interests. Students will select areas of study in consultation with their supervisor. These areas should not significantly overlap with material covered in courses currently offered at York University and undergraduate or graduate courses taken by the student either at York University or elsewhere. Directed reading courses require a completed directed reading form. Students should return the completed form to the graduate program assistant. A printout of an email confirming approval can be used in lieu of a signature on the form.
The Electrical and Computer Engineering research project course (EECS 6400) spans two terms. This course provides an introduction to research methods and methodology in Electrical and Computer Engineering. Under the direction of the Electrical and Computer Engineering research project committee, which consists of the professors Ali Hooshyar (chair), Gerd Grau and Matthew Kyan, students engage in supervised research under one or two members of the graduate program. The topic of the project must be distinct from any assignments in any of the other courses and must also be distinct from the thesis. The research project course requires a completed project proposal form, which needs to be approved by the supervisor(s) and the chair of Electrical and Computer Engineering research project committee. Completed forms should be returned to the graduate program assistant. A printout of an email confirming approval can be used in lieu of a signature on the form.
Students are required to complete the course selection form in consultation with their supervisor. Completed forms should be returned to the graduate program assistant.
Students may request to take courses offered by other graduate programs at York University. Such a course requires a completed request form, which needs to be approved by the course instructor, the graduate program director of the program offering the course and the graduate program director. Completed forms should be returned to the graduate program assistant. A printout of an email confirming approval can be used in lieu of a signature on the form.
Students may request to take a course offered at another university in Ontario. Students are required to complete the Ontario visiting graduate student application form. Completed forms should be returned to the graduate program assistant. Only if all the conditions listed on the second page of the form are satisfied, the graduate program director will approve the request. More information can be found at the website of the Faculty of Graduate Studies.
|September 8, 2016||classes start|
|September 16, 2016||last date to hand in the directed reading form|
|September 21, 2016||last date to add course without permission of instructor|
|October 1, 2016||last date to hand in the course selection form|
|October 5, 2016||last date to add course with permission of instructor|
|October 10, 2016||no classes (Thanksgiving)|
|October 27-30, 2016||no classes (Fall reading days)|
|November 11, 2016||last date to drop course without receiving a grade|
|December 5, 2016||classes end|
|December 7-22, 2016||exam period|
|January 5, 2017||classes start|
|January 13, 2017||last date to hand in the directed reading form|
|January 18, 2017||last date to add course without permission of instructor|
|February 1, 2017||last date to add course with permission of instructor|
|February 10, 2017||last date to drop EECS 6400 (started in the Fall) without receiving a grade|
|February 18-24, 2017||no classes (reading week)|
|March 10, 2017||last date to drop course without receiving a grade|
|April 5, 2017||classes end|
|April 7-24, 2017||exam period|
|May 15, 2017||last date to hand in the directed reading form and the project proposal form|
|July 7, 2017||last date to drop EECS 6400 (started in the Winter) without receiving a grade|