# Summary
📗 Examples and quizzes:
E5
📗 Math homework:
M6 and
M7
📗 Programming homework:
P3
📗 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