# Summary

M6, M7,
P3, P6,
Q9, Q10, Q11, Q12, Q13, Q14,

# Lectures

Lecture 10: Slides.
Lecture 11: Slides.
Lecture 12: Slides.

Lecture 10 Part 1 (Generative Models): Link
Lecture 10 Part 2 (Natural Language): Link
Lecture 10 Part 3 (Sampling): Link
Lecture 11 Part 1 (Probability Distribution): Link
Lecture 11 Part 2 (Bayesian Network): Link
Lecture 11 Part 3 (Network Structure): Link
Lecture 11 Part 4 (Naive Bayes): Link
Lecture 12 Part 1 (Hidden Markov Model): Link
Lecture 12 Part 2 (HMM Evaluation): Link
Lecture 12 Part 3 (HMM Training): Link
Lecture 12 Part 4 (Recurrent Neural Network): Link
Lecture 12 Part 5 (Backprop Through Time): Link
Lecture 12 Part 6 (RNN Variants): Link


Image by xkcd via towards data science

# Other Materials

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
Random Integer: Link
Multi Armed Bandit (don't play this): Link
Autonomous driving: Link
Stochastic growth model: Link
Q Learning: Link
Deep reinforcement learning: Link
Learning games: Link

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