# Summary

📗 Examples and quizzes: E9 and E10 and E11 and E12
📗 Math homework: M6 and M7
📗 Programming homework: P3
📗 Midterm page: Next: W4
📗 Tuesday to Friday lectures: 12:30 to 1:45, 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.
Lecture 23: Slides.
Lecture 24: Slides.
(Sorry about the inconsistent file names: these topics were not covered in 2019)

📗 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
Lecture 23 Part 1: Link
Lecture 23 Part 2: Link
Lecture 23 Part 3: Link
Lecture 24 Part 1: Link
Lecture 24 Part 2: Link
Lecture 24 Part 3: Link

📗 Notes
Typo: Lecture 23 Viterbi Algorithm the recursive formula should be V_t,k = max ... V_t-1,k, not V_1,k.
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.
Lecture 23 Part 3 and Lecture 24 Part 2 and 3 about training algorithms for Hidden Markov Models and Recurrent Neural Networks are completely optional. Treat the other parts as reviews for the previous lecture, no questions directly related to HMM and RNN will be on the exams. 
Lectures 11 and 12 videos will not be uploaded until after the midterm.

# 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
RNN Visualization: Link
LTSM and GRU: 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 probability 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