Syllabus
Instructors
Instructor: Qiang Sun
Lectures: Wed, 12:00 - 13:00, IA2050; Fri, 10:00 - 12:00, IA2160
Office Hours: Wed, 15:00 - 16:00, IA4030; Fri, 15:00 - 16:00, IA4030
Email: qiang.sun@utoronto.ca
TA: TBD, TBD
Please email if standard office hours times do not work for you.
Instructional Methods
In Person. Some classes may be scheduled on Zoom in unforeseen circumstances.
Course Description
This course is a topic course, whose contents evolve from year to year. The course usually consists of both theoretical and computational components. This year, we will focus on three pillars: 1. Fundamentals, such as statistical principles and models 2. Optimization and generative learning 3. Generative modeling and learning
Course Materials
All materials are available as publicly available research papers linked from the course schedule.
Textbook
Reference Books
Prerequisites and Corequisites
This course assumes basic training in linear algebra, probability theory, and statistical inference.
Course Assignments and Requirements
- Particpation: 10%
- Assignments: 30%
- midterm Exam: 30%
- Final Project: 30%
Student Survey of Teaching
You will have multiple opportunities to provide feedback on your experience in this course. Suggestions and constructive criticism are encouraged throughout the course and may be particularly valuable early in the semester. To that end, we may use surveys and/or reflection assignments to gather input on what is working well and what could be improved. You will also be asked to complete an end-of-semester, online Student Survey of Teaching, which could inform modifications to this course (and other courses that I teach) in the future.
Attendance Policy
All course materials will be shared online after class. You are expected and encouraged to attend and participate in class. There are 10% points for lecture participation. If you have any problems which will cause you to miss a class/lab or an assignment deadline please email me.
Subject to Change Statement
The schedule for the class beyond next week is subject to change. Please consult the course website to know the latest version of the schedule and syllabus. Any changes in assignments and syllabus policies will conveyed via Quercus announcements.
Student Resources and University Policies
Please visit the Student Resources website for a list of student resources and university policies.
Academic Integrity
Academic misconduct of any kind will automatically result in a 0 score on the assignment and your actions will be reported to the university. You can discuss with peers, however your work must be your own. Posting your assignments to internet discussion lists / forums / chatrooms is considered academic misconduct. Directly sharing your solutions with others is also considered academic misconduct.