EECS 5324 An Introduction to Robotics – CANCELLED
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.
|Michael Jenkin||TR||8:30||90||In person – Location TBD||2|
EECS 5326 Artificial Intelligence
This course will be an in-depth treatment of one or more specific topics within the field of Artificial Intelligence.
|Yves Lesperance||TR||10:00||90||In person – Location TBD||2|
EECS 5327 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 pattern recognition problem in a diversity of application areas.
|Ruth Urner||MW||13:00||90||In person – Location TBD||2|
EECS 5391 Simulation and Animation for Computer Games
This course covers the basic principles and practices related to motion synthesis and motion control for animated objects, such as those that appear in films and computer games.
|Petros Faloutsos||TR||17:30||120||In person – Location TBD||2|
EECS 5431 Mobile Communication
This 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.
|U.T. Nguyen||R||14:30||180||In person – Location TBD||3|
EECS 5443 Mobile User Interfaces
This 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 MacKenzie||TR||14:30||90||In person – Location TBD||3|
EECS 5640 Medical Imaging Techniques: Principles and Applications
This course introduces principles of medical imaging, focusing on major imaging modalities including ultrasound, X-ray radiography, computed tomography, magnetic resonance imaging, and nuclear medicine imaging. The course covers the physics and engineering aspects of how various imaging signals are acquired and processed in order to form medically useful images. The course also covers essentials of medical image analysis.
|Ali Sadeghi-Naini||TR||11:30||90||In person – Location TBD||N/A|
EECS 6127 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.
|Ruth Urner||MW||10:00||90||In person – Location TBD||1|
EECS 6154 Digital Image Processing: Theory and Algorithms
Fundamental image processing theories and algorithms. Signal representation using transforms, wavelets and frames is overviewed. Signal reconstruction methods using total variation, sparse coding and low-rank prior, based on convex optimization, are discussed. Applications include image compression, restoration and enhancement.
Prior background in digital signal processing (EECS 4452 or equivalent) and numerical linear algebra is strongly recommended.
|Gene Cheung||MW||13:00||90||In person – Location TBD||1|
EECS 63XX Privacy in Sociotechnical Systems (tentative)
Course description forthcoming.
|Yan Shvartzshnaider||MW||14:30||90||In person – Location TBD||2|
EECS 6322 Neural Networks and Deep Learning
This course covers the theory and practice of deep learning and neural networks. Topics covered include training methods and loss functions, automatic differentiation and backpropagation, network architectures for different learning problems, validation, model selection and software tools. Prerequisites: EECS 5327 or EECS 6327 or permission of instructor.
|Kosta Derpanis||TR||16:00||90||In person – Location TBD||2|
EECS 6323 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: EECS5323 3.0 Introduction to Computer Vision.
|John Tsotsos||MW||11:30||90||In person – Location TBD||2|
EECS 6324 / PSYC 6225 Computational Models of Visual Perception
This course introduces the student to state-of-the-art computational models for human visual processing, and the tools required to advance the state of the art.
|James Elder||W||14:30||180||In person – Location TBD||2|
EECS 6330 Critical Technical Practise: Computer Accessibility and Assistive Technology
This course examines issues of technological design in computer accessibility and computational forms of assistive technology (hardware and/or software). Students learn to critically reflect on the hidden assumptions, ideologies and values underlying the design of these technologies, and to analyse and to design them.
|Melanie Baljko||MW||14:30||90||In person – Location TBD||2|
EECS 6414 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.
|Jarek Gryz||TR||11:30||90||In person – Location TBD||3|
EECS 6448 Data Science for Software Requirements
Course description forthcoming.
|Maleknaz Nayebi||MW||16:00||90||In person – Location TBD||3|
EECS 6802 Implantable Biomedical Microsystems – CANCELLED
This course provides an introduction to implantable biomedical microsystems, their design, and applications. Engineering design, implementation, and test of a wide variety of biomedical implants is discussed. This includes system-level and architectural design, circuit design (analog and mixed-signal, generic/application-specific), wireless interfacing (power and bidirectional data telemetry), hardware-embedded biological signal processing, design & implementation of non-circuit modules such as microelectrode arrays.
|Amir Sodagar||MW||10:00||90||In person – Location TBD||N/A|
EECS 6808 Engineering Optimization
This course introduces classical and modern optimization techniques to solve engineering analysis and design problems. Students will learn how to formulate single- and multi-variable engineering problems as optimization problems and how to solve such problems using appropriate optimization techniques. The details of specific techniques required to solve the formulated problems will be discussed from theory and application points of view.
|Ali Sadeghi-Naini||TR||17:30||90||In person – Location TBD||N/A|
PHIL 5340 Ethics and Societal Implications of Artificial Intelligence – for AI specialization students
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).
|TBD||R||14:30||180||In person – Location TBD||–|