Reading 07: k-Nearest Neighbors, Support Vector Machines, and Clustering

Readings

• • Slides
• • PML - Sections 16.1, 17.3
• • ISLP - Sections 3.5, 4.7.6 and Chapter 9
• • UML - Chapters 15, 19
• • KNN (HdM)
• • SVMs (HdM)
• • An Idiot's Guide to Support Vector Machines

Videos

• • Pantopto
• • sp25 Panopto
• • Harvard CS50 (0:00 - 12:30 and 33:29 - 39:41)
• • MIT 6.034 SVMs
• • MIT 6.034 KNNs
• • Cornell CS4780 KNNs
• • Cornell CS4780 SVMs 1
• • Cornell CS4780 SVMs 2
• SVMs (StatQuest)
• KNN (StatQuest)

Notebooks

Blogposts

• • Kernel Methods (Raschka)
• • Machine Learning Algorithms Explained: Support Vector Machine
• • K-Nearest Neighbor (KNN) Explained

Papers

• • (1963) A Note on One Class of Perceptrons (Vapnik and Chervonenkis)
• • (1992) A Training Algorithm for Optimal Margin Classifiers (Boser et al.)
• • (1995) Support-Vector Networks (Cortes and Vapnik)

Readings

• • Slides
• • FCA - Section 10.3
• • PML - Chapter 21
• • ISLP - Section 12.4
• • UML - Chapter 22

Videos

• • sp25 Panopto
• • Hierarchical (StatQuest)
• • K-Means (StatQuest)

Notebooks

• • Clustering Visualizations
• • GMM Clustering
• • Clustering (rasbt)

Blogposts

• Clustering: How It Works (In Plain English!)