# Summary

📗 Examples and quizzes: E5
📗 Math homework: M6 and M7
📗 Programming homework: P3
📗 Monday lecture: 5:30 to 8:30, Guest Link
📗 Tuesday programming office hours: 5:00 to 6:00, Java Guest Link, Python Guest Link
📗 Wednesday math homework office hours: 5:00 to 6:00, Guest Link
📗 Thursday math homework office hours: 5:00 to 6:00, Guest Link
📗 Friday office hours for other things: 5:00 to 6:00, Guest Link

# Lectures

📗 Slides
Lecture 9: Slides.
Lecture 10: Slides.

📗 Videos
Lecture 9 Part 1: Link
Lecture 9 Part 2: Link
Lecture 9 Part 3: Link
Lecture 10 Part 1: Link
Lecture 10 Part 2: Link
Lecture 10 Part 2.5: Link
Lecture 10 Part 3: Link

📗 Notes
I made a video going though how auto-grading is done for P3 in case you are interested: Link.
The end of Lecture 10 Part 2 contains a mistake: the part is re-recorded and uploaded as Lecture 10 Part 2.5. Chow-Liu type MST algorithms will not appear on the midterm.
For Zipf's Law: the f could also be the proportion or frequency (count / total), not just the count.

# Other Materials

📗 Relevant websites
Markov Chain: Link
Matrix Calculator: Link
Zipf's Law: Link
Google N-Gram: Link
Simple Bayes Net: Link, Link 2
Pathfinder: Link
Minimum Spanning Tree: Link

📗 YouTube videos from 2019
How to find maximum likelihood estimates for Bernoulli distribution? Link
How to generate realizations of discrete random variables using CDF inversion? Link
Example: How to compute the joint probaility given the conditional probability table? Link
Example (Quiz): How to compute conditional probability table given training data? Link
Example (Quiz): How to do inference (find joint and conditional probability) given conditional probability table? Link
Example (Quiz): How to find the conditional probabilities for a common cause configuration? Link