# Summary

📗 Examples and quizzes: E8
📗 Math homework: M8
📗 Programming homework: P4
📗 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 11: Slides.
Lecture 12: Slides.

📗 Videos
Lecture 11 Part 1: Link
Lecture 11 Part 2: Link
Lecture 11 Part 3: Link
Lecture 12 Part 1: Link
Lecture 12 Part 2: Link
Lecture 12 Part 3: Link

📗 Note
The video going through P4 grading: Link.
The total distortion sometimes are defined as the sum of squared distances for Euclidean distances: that is the one I used for the gradient descent step derivation.

# Other Materials

📗 Relevant websites
Image Segmentation: Link 1, Link 2
K Means Clustering: Link
K Gaussian Mixture: Link
Tree of Life: Link 1, Link 2
Generative Adversarial Net: Link 
Principal Component: Link 1, Link 2
Eigen Face: Link 1, Link 2
t-distributed Stochastic Neighbor Embedding: Link
Swiss Roll: Link
tSNE Demo: Link
PCA Proofs from Professor Jerry Zhu's 540 notes: PDF File

📗 YouTube videos from 2019
What is the relationship between Naive Bayes and Logistic Regression? Link
What is the relationship between K Means and Gradient Descent? Link
Why is PCA solving eigenvalues and eigenvectors? Part 1, Part 2, Part 3
How to update distance table for hierarchical clustering? Link
How to update cluster centers for K-means clustering? Link
How to compute projection? Link
How to compute new features based on PCA? Link