Class times: Thursday, 5:30 – 7:45pm, usually fully online, sometimes in person option in LL 311 (will be announced ahead)
Instructor: Prof. Daniel D. Leeds (my homepage)
Office: online generally
E-mail:
Office hours: Usually Tues 4-5pm online; Sometimes Thurs 4-5pm in person

Full syllabus

Course text: No book will be required, but the following will be useful as references.

• "Pattern Recognition and Machine Learning" by Bishop
• "Machine Learning: A Probabilistic Approach" by Murphy

• In Python, you can use numpy, scipy and matplotlib packages to write acceptable code for this class in Python. (You also may used the pandas package; you may not use packages like tensorflow for this class.)

1. Resources
2. Announcements
3. Slides
4. Assignments

Announcements:
August 26, 8:30am: I have shifted plans and will focus on providing content primarily on Blackboard. You can find extra details about our first lecture in the announcements there, as well as a link to the Zoom meeting for the lecture (coming this Thursday!).
August 23, 10:00am: The first two lectures will be fully online via zoom. A zoom link will be made available by e-mail and Blackboard, but not on this web site.

Slides: Check slides after lectures as well for updates made during class!
I have not found any one textbook or online resource to be an optimal match for the material we cover at the mathematical level we cover it. However, Andrew Ng's lecture notes, available on Stanford's Machine Learning course web site, often can be a helpful online read. I recommend these notes as well as looking through one of our course textbooks.

	Supplementary reading
Lecture 1, Course logistics, background math, intro to classifiers.

Assignments: