We will aim to folow the first two parts of the textbook (see below). Following the structure of the book, the first part of the course will be devoted to the general theory of machine learning, and in the second part we will go over some basic algorithms that are common in ML and explain the theory underlying them. The first 20 chapters of the book are all important for understanding machine learning. I hope to cover all of them, but I cannot guarantee it.
2) Overlap with CS480: I do not think there is going to be much of that. This course focuses on theory not implementation. I aim to explain the principles behind common ML algorithms rather than teaching how to use them in practice. This is a rather different focus than that of CS480 (even when both courses discuss the same algorithms).
3) There will be no coding and no implementations of algorithms in this course.
The Zoom links are posted on Piazza.
Details on how to access the tutorials will be shared with you on Piazza and LEARN.
|Tuesdays 1:30 p.m. – 2:30 p.m.|
|Assignments||Average of the 4 best of 5 assignments - 40%|
|Mid-Term Assessment||Counts only if better mark than the final - 20%|
|Final Assessment||40% or 60% if better than the midterm|
All course material will be posted on the LEARN site.
Assignment 1 posted: September 18, Submission deadline: October 1
Assignment 2 posted: October 2, Submission deadline: October 22
Assignment 3 posted: October 23 Submission deadline: November 5
Assignment 4 posted: November 6, Submission deadline: November 19
Assignment 5 posted: November 23, Submission deadline: December 7
The midterm will be posted on October 18 and due on October 19
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