# Summary

📗 Examples and quizzes: E3
📗 Math homework: M4
📗 Programming homework: P2
📗 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 5: Slides.
Lecture 6: Slides.

📗 Videos
Lecture 5 Part 1: Link
Lecture 5 Part 2: Link
Lecture 5 Part 3: Link
Lecture 6 Part 1: Link
Lecture 6 Part 2: Link
Lecture 6 Part 3: Link

📗 Notes
I made a video going though how auto-grading is done for P2 in case you are interested: Link.
I did not talk about k-d tree this time, so if you would like to implement kNN, the Wikipedia page has some useful details: Link
For Christopher Nolan fans: the entropy (information theory) we are discussing for decision trees is different from the entropy (second law of thermodynamics) related to the movie TENƎꓕ. Maybe someone could explain to me how they are related? Wikipedia?

# Other Materials

📗 Relavent websites
Support Vector Machine: Link
Kernel Trick Video: Link
RBF Kernel SVM Demo: Link
Art or garbage game: Link
Decision Tree: Link
Random Forrest Demo: Link
K Nearest Neighbor: Link
Map of Manhattan: Link
Voronoi Diagram: Link
KD Tree: Link

📗 YouTube videos from 2019
How to find the margin expression for SVM? Link
Why does the kernel trick work? Link
Example (Quiz): Compute SVM classifier Link
Example (Quiz): Kernel SVM for XOR operator Link
Example (Quiz): Kernel matrix to feature vector Link
Example (Quiz): Entropy computation Link
Example (Quiz): Decision tree for implication operator Link
Example (Quiz): Three nearest neighbor Link