CS M146 - Machine Learning

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Table of contents

• 00 - Intro to ML - lec. 1
• 01 - Linear Regression - lec. 1,2,3
• 02 - Generalization - lec. 3,4
• 03 - Perceptron - lec. 5
• 04 - Logistic Regression - lec. 5,6
• 05 - Bias-Variance Tradeoff - lec. 6
• 05 - K-Nearest Neighbor - lec. 6,7
• 06 - Decision Trees - lec. 7,8
• 07 - Kernels - lec. 9,10
• 08 - SVM - lec. 11, 12, 13
• 09 - Ensembles and Boosting - lec. 14
• 10 - Clustering - lec. 15
• 11 - Dimensionality Reduction (PCA) - lec. 16
• 12 - Neural Networks - lec. 17
• 13 - Backpropogation - lec. 18
• 14 - Deep Learning - lec. 19
• 15 - Generative Models - lec. 20