Slides are adapted from Yuxin Chen's version.
Lecture 0: Logistics
Lecture 1-1: Introduction
Lecture 1-2: Linear regression
Lecture 2-1: Linear regression and maximum likelihood estimation
Lecture 2-2: Regularized linear regression and maximum a posteriori
Lecture 3-1: Model selection
Lecture 3-2: Logistic regression
Lecture 4-1: Generative models for classification
Lecture 4-2: Support vector machine (credit: Simon Du)
Lecture 5-1: Kernel methods (credit: Simon Du)
Lecture 6-1: Multi-layer perceptrons (credit: Simon Du)
Lecture 6-2: Convolutional neural networks (credit: Simon Du)
Lecture 7-1: Decision trees
Lecture 7-2: Bagging and random forests
Lecture 8-1: Boosting
Lecture 8-2: Clustering: k-means and Gaussian mixture models
Lecture 9-1: Principal component analysis
Lecture 9-2: Spectral methods