CMPUT 466/566: Machine Learning
Instructor: Lili Mou
The course will be delivered remotely. UofA members (for credit or auditing) enjoy a private Google Meet lecture room, QA sessions with the instructor and TAs, as well as other social events.
- Prerequisites will not be waived for crediting.
- The course is full at this moment for students taking for credit. To be listed on the waiting list, the student needs to send an email to explain his/her math and programming background (courses taken since college study). If satsifactory, the instructor will ask the student to fill in a Google Form. If selected, the instructor will send an email to the student. Notice that the chance of enrollment is considered as very low.
- Official auditing is always welcome. Just send the audit form to the instructor.
- Unofficial sitting through the course is also welcome, and you can find the information on this page.
- Adding to eClass is possible for UofA members (students and staff). Just send an email to the instructor.
- For non-UofA members, you would not be able to access eClass. But lectures are open accessible to anyone who has Internet access.
12:30 PM -- 1:50 PM, Tuesday and Thursday, 1-Sep-2020 ~ 7-Dec-2020
Machine learning teaches a machine to learn from previous experience and makes a prediction for (possibly new) data. This course covers standard materials of a “Machine Learning” course, such as linear regression, linear classification, as well as non-linear models. In the process, we will have a systematic discussion on training criteria, inference criteria, bias-variance tradeoff, etc. The goal of the course is to build a solid foundation of machine learning, so there would be intensive math derivations in lectures, assignments, and exams.
Syllabus and Open-Access Information
Please refer to this link
- 1. Introduction [pdf, video]
- 2. Linear Rergression (Formulation)
- 3. Linear Rergression (Convexity)
- 4. Linear Rergression (Probabilistic View)
- 5. Linear Rergression (Regularization)
- 6. Linear Rergression (Bayesian Learning)
- 7. Linear Classification (formulation)
- 8. Linear Classification (logistic/softmax regression)
- 9. Exponential Family and GLIMS
- 10. Generative Models