# Summary

📗 Monday to Thursday lectures: 12:30 to 1:45, Zoom Link (Weeks 1 to 7), Zoom Link (Week 8) 
📗 Monday to Sunday office hours: 2:00 to 3:00, Zoom Link (Weeks 1 to 7), Zoom Link (Week 8) 
📗 Personal meeting room: always open, Zoom Link
📗 Quiz (use your wisc ID to log in (without "@wisc.edu")): Socrative Link
📗 Math Homework: M8,
📗 Programming Homework: P4,
📗 Examples and Quizzes: Q15, Q16,

# Lectures

📗 Slides
Lecture 15: Slides.
Lecture 16: Slides.

📗 Videos
Lecture 15 Part 1 (Unsupervised Learning): Link
Lecture 15 Part 2 (Hierarchical Clustering): Link
Lecture 15 Part 3 (K Means Clustering): Link
Lecture 16 Part 1 (Dimensionality Reduction): Link
Lecture 16 Part 2 (Principal Component): Link
Lecture 16 Part 3 (Non-linear PCA): Link

📗 Notes

Image by bismart

# 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 and 2020
How to compute value function given policy? Link
How to compute optimal value function? Link
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