Courses

Courses in fall 2017
coursetitleinstructordaystart timedurationlocationgroup
EECS 5323
Computer VisionThis course introduces the basic concepts in computer vision. Primarily a survey of current computational methods, we 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 consider some issues in "high-level" vision by examining current high-level vision systems.
Richard WildesMWF
M or T
9:30
14:30
60
60
CBChemistry Building
115
LASLassonde Building
1004
2, 6
EECS 5326
Artificial IntelligenceThis course will be an in-depth treatment of one or more specific topics within the field of Artificial Intelligence.
Zbigniew StachniakTR11:3090
PSEPetrie Science and Engineering Building
321
2
EECS 5443
Mobile User InterfacesThis course teaches the design and implementation of user interfaces for touchscreen phones and tablet computers. Students develop user interfaces that include touch, multi-touch, vibration, device motion, position, and orientation, environment sensing, and video and audio capture. Lab exercises emphasise these topics in a practical manner.
Scott MacKenzieTR
R
10:00
11:30
90
120
VHVari Hall
3009
LASLassonde Building
1004
3, 4
EECS 5501
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.
Mokthar AboelazeTR10:0090
PSEPetrie Science and Engineering Building
321
3, 4
EECS 6002
Machine Learning TheoryThis 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.
Ruth UrnerMW11:3090
BRGBergeron Centre for Engineering Excellence
211
1
EECS 6115
Computational ComplexityThis course provides an introduction to complexity theory, one of the most important and active areas of theoretical computer science. Students learn basic concepts of the field and develop their abilities to read and understand published research literature in the area and to apply the most important techniques in other areas.
Patrick DymondTR14:3090
CCCalumet College
335
1
EECS 6329
Advanced Human-Computer InteractionThis course examines advanced concepts and technologies for human-computer interaction. Students will learn about advanced input and output devices (e.g. for mobile computing and/or virtual reality), about advanced design methods, how to implement effective interfaces, and how to perform rapid, effective iterative user tests.
Scott MacKenzieTR16:0090
CCCalumet College
335
2, 6
EECS 6390A
Knowledge RepresentationThe 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.
Yves LesperanceMWF10:3060
BCBethune College
228
2
EECS 6412
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.
Aijun AnMW14:3090
CBChemistry Building
120
3
EECS 6414
Data Analytics and VisualizationData 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.
Manos PapagelisM17:30180
RRoss Building
S102
3
EECS 6421
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.
Parke GodfreyMW16:0090
BCBethune College
228
3, 4
EECS 6590A
High Performance Computer NetworksThis course focuses on high performance computer networks. It presents a comprehensive study of modern high speed communication networks that is capable of providing data, voice, and video services. It also covers mobile and wireless communication networks
U.T. NguyenTR14:3090
MCMcLaughlin College
101A
3, 4
EECS 6611
Mixed-Signal Microsystems DesignThis course highlights design and analysis of major mixed-signal microsystems and their building blocks. Topics include introduction to design and analysis of switched-capacitor circuits, sampling circuits and architectures, comparators, continuous-time and discrete-time active filters, fundamentals of digital-to-analog and analog-to-digital data conversion, Nyquist-rate, multi-step, pipeline, and oversampled A/D architectures.
Hossein KassiriTR13:0090
CCCalumet College
335
5
EECS 6701
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.
John LamW11:30180
CCCalumet College
335
5
Courses in winter 2018
coursetitleinstructordaystart timedurationlocationgroup
EECS 5111
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.
George TourlakisMW14:3090
ACEAccolade East Building
005
1
EECS 5324
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.
Burton MaMWF13:3060
SLHStedman Lecture Hall
C
2, 6
EECS 5327
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.
Ruth UrnerTR10:3090
HNEHealth, Nursing and Environmental Studies Building
037
2, 6
EECS 5331
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.
Petros FaloutsosTR
T or W
14:30
9:30 or 13:30
90
120
PSEPetrie Science and Engineering Building
321
LASLassonde Building
1004
2, 6
EECS 5351
Human-Computer InteractionThis course introduces the concepts and technologu necessary to design, manage and implement interactive software. Students work in small groups and learn how to design user interfaces, how to realize them and how to evaluate the end result. Both design and evaluation are emphasized.
Melanie BaljkoTR13:0090
CCCalumet College
211
2, 6
EECS 5431
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.
Peter LianR17:30180
LSBLife Science Building
101
3, 4
EECS 5442
Real-Time Systems Practice (cancelled)Introduction 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.
Jia XuTR11:3090
CLHCurtis Lecture Hall
110
3, 4
EECS 5443
Mobile User InterfacesThis course teaches the design and implementation of user interfaces for touchscreen phones and tablet computers. Students develop user interfaces that include touch, multi-touch, vibration, device motion, position, and orientation, environment sensing, and video and audio capture. Lab exercises emphasise these topics in a practical manner.
Scott MacKenzieTR14:3090
CCCalumet College
108
3, 4
EECS 5612
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.
Amir SodagarTR
F
11:30
16:30
90
120
CBChemistry Building
122
BRGBergeron Centre for Engineering Excellence
321
5
EECS 6222
Coding and Information Theory (cancelled)This course introduces students to the fundamentals of information theory, as well as methods for achieving information-theoretic results using source codes and channel codes. Students will learn Shannon's source coding and channel coding theorems, as well as the mathematical machinery required to prove these and other information theoretic results. Students will also be exposed to source coding techniques, as well as channel coding techniques for state-of-the-art systems. Advanced topics such as multiterminal (Slepian-Wolf) source coding and rateless codes will also be covered, time permitting.
Andrew EckfordMWF9:3060
MCMcLaughlin College
101A
1, 4
EECS 6323
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.
Marcus BrubakerMWF10:3060
RRoss Building
S501
2, 6
EECS 6327
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.
Hui JiangTR16:0090
BCBethune College
228
2, 6
EECS 6444
Mining Software Engineering DataSoftware engineering data (such as source code repositories, execution logs, performance counters, developer mailing lists and bug databases) contains a wealth of information about a project's status and history. Applying data mining techniques on such data, researchers can gain empirically based understanding of software development practices, and practitioners can better manage, maintain and evolve complex software projects.
Jack JiangMW17:3090
RRoss Building S501
3, 4
EECS 6601
NanoelectronicsThe sustained demand for increased memory and computational power has driven the physical size of electronic components to nanoscale dimensions. The need to investigate size effects and to find viable ways to manufacture at the nanoscale has also led to the discovery of new phenomena and functionality. This course covers electronic transport and other properties in nanoscale systems, devices, characterization and fabrication techniques. Topics to be covered include quantum confinement, quantum dots, nanowires, 2D electron gases, single electron transistors, spintronic devices, electronic transport and optical properties, nanoscale materials, top-down and bottom-up fabrication approaches.
Gerd GrauMW11:3090
RRoss Building
S125
5
Groups of courses
numbername
1Theory of Computing & Scientific Computing
2Artificial Intelligence & Interactive Systems
3Systems: Hardware & Software
4Computer Systems Engineering
5Electrical Engineering
6Interactive Systems Engineering

MSc Students (thesis option)

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.

MSc Students (project option)

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.

MASc Students

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.

PhD Students

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). 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.

Directed Reading Course

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.

Research Project Course

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 Gerd Grau (chair), Amir Sodagar and Jia Xu, 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.

Course Selection

Students are required to complete the course selection form in consultation with their supervisor. Completed forms should be returned to the graduate program assistant.

Courses in Another Graduate Program

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.

Courses at Another Ontario University

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.

Important Dates

September 7, 2017classes start
September 15, 2017last date to hand in the directed reading form
September 20, 2017last date to add course without permission of instructor
October 4, 2017last date to add course with permission of instructor
October 9, 2017no classes (Thanksgiving)
October 13, 2017last date to hand in the course selection form
October 26-29, 2017no classes (Fall reading days)
November 10, 2017last date to drop course without receiving a grade
December 4, 2017classes end
December 6-21, 2017exam period
January 4, 2018classes start
January 12, 2018last date to hand in the directed reading form
January 17, 2018last date to add course without permission of instructor
January 31, 2018last date to add course with permission of instructor
February 9, 2018last date to drop EECS 6400 (started in the Fall) without receiving a grade
February 17-23, 2018no classes (reading week)
March 9, 2018last date to drop course without receiving a grade
April 4, 2018classes end
April 9-23, 2018exam period

Other important dates can be found at the website of the Faculty of Graduate Studies.

Additional Information

More information about graduate courses and grading can be found at the website of the Faculty of Graduate Studies.