In this lecture, we cover

Slides:

Slides [2021], Follow-up slides [2021]

Part 1: Probability (probability spaces, random variables, moments)

Part 2: Probabilistic dependence, directed graphical models (Bayes networks)

Corrections:

On Slide 62 in the recording, the title was wrong. It has been corrected in the slides above.

All slides are subject to change until the lecture is given.

Mohri et al., Foundations of Machine Learning, Appendix C
Pearl, d-separation without tears
Koller & Friedman, Probabilistic Graphical Models: Principles and Techniques, Chapter 3 and course slides
Pearl, Causality (2nd ed.), Chapters 1.1–1.2
Brady Neal's videos on Graphical models

In 2020, this course was given as a PhD course. The video for this module from that run is available below, and the slides are available here: [introduction, follow-up]. Make note that this does not completely reflect the content of this year's course.