# Summary

📗 Examples and quizzes: E1 and E2 and E3 and E4
📗 Math homework: M1 and M2 and M3
📗 Programming homework: P1
📗 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 1: Slides.
Lecture 2: Slides.
Lecture 3: Slides.
Lecture 4: Slides.

📗 Videos
Lecture 1 Part 1 (Admin): Link
Lecture 1 Part 2 (Supervised learning): Link
Lecture 1 Part 3 (Perceptron learning): Link
Lecture 2 Part 1 (Loss functions): Link
Lecture 2 Part 2 (Logistic regression): Link
Lecture 2 Part 3 (Convexity): Link
Lecture 3 Part 1 (Neural Network): Link
Lecture 3 Part 2 (Backpropogation): Link
Lecture 3 Part 3 (Multi-Layer Network): Link
Lecture 4 Part 1 (Stochastic Gradient): Link
Lecture 4 Part 2 (Multi-Class Classification): Link
Lecture 4 Part 3 (Regularization): Link

📗 Notes
I recorded a video going through the M1 questions while review some of the math concepts: Link, you do not have to watch this and lecture 2 part 3 if you have taken Calculus 2 and (Linear) Algebra 1.
I recorded a video talking about P1 and how it is graded: Link. In case you are curious, I explained how the auto-grading scripts (JavaScript) grade P1 and M1. You do not have to watch it to solve P1.
Last year's perceptron update rule explanation perhaps is clearer: Link
I forgot to mention in the video that we are starting with machine learning (the more difficult half) and we will cover search and games (the easier half) after the midterm. The order of the topics is reversed from the course in fall and winter semesters, but by the end of the summer, we will have covered the same materials.
For Robert Downey Jr. fans, his 2 min intro to AI is great: Link, although it's not what we will do in this course
The video series by 3Blue1Brown on Neural Networks are really good: Playlist. 

# Other Materials

📗 Relavent websites
Which face is real? Link
Guess two-thirds of the average? Link
Gradient Descent. Link
Eigenvalue in Endgame. Link
Plot 3D functions: Link
Neural Network: Link
Neural Network Videos by Grant Sanderson: Playlist (Thanks Dan Drake for the recommendation)
Stochastic Gradient Descent: Link
Overfitting: Link
Neural Network Snake: Link
Neural Network Car: Link
Neural Network Flappy Bird: Link
Neural Network Mario: Link

Stat



📗 YouTube videos from 2019
Why does the (batch) perceptron algorithm work? Link
Why cannot use linear regression for binary classification? Link
Why does gradient descent work? Link
How to derive logistic regression gradient descent step formula? Link
Example (Quiz): Perceptron update formula Link
Example (Quiz): Gradient descent for logistic activation with squared error Link
Example (Quiz): Computation of Hessian of quadratic form Link
Example (Quiz): Computation of eigenvalues Link
Example (Homework): Gradient descent for linear regression Link
How to construct XOR network? Link
How derive 2-layer neural network gradient descent step? Link
How derive multi-layer neural network gradient descent induction step? Link
Comparison between L1 and L2 regularization. Link
Example (Quiz): Cross validation accuracy Link

📗 Math and Statistics Review
Checklist: Link, "math crib sheet" under "10/11"
Multivariate Calculus: Textbook, Chapter 16 and/or (Economics) Tutorials, Chapters 2 and 3.
Linear Algebra: Textbook, Chapters on Determinant and Eigenvalue.
Probability and Statistics: Textbook, Chapters 3, 4, 5.