This is the webpage for a directed reading program (DRP) I am leading during MIT's Independent Activities Period (IAP 2021). The subject is an introduction to machine learning.

**[B] Bishop,***Pattern Recognition and Machine Learning*(2006).

- (1/4) Overfitting, regularization, and model selection; curse of dimensionality; decision theory. [B] 1.1, 1.3-1.5.
- (1/6) Linear regression; bias-variance trade-off. [B] 3.1-3.2.
- (1/8) Bayesian linear regression; Bayesian model comparison. [B] 3.3-3.4, 3.6.
- (1/11) Fisher's linear discriminant; perceptron. [B] 4.1.
- (1/13) Logistic regression. [B] 4.2.1-4.2.3, 4.3.1-4.3.4.
- (1/18) Neural networks; backpropagation. [B] 5.1-5.2.2, 5.2.4-5.3.3.
- (1/20) Regularization in neural networks. [B] 5.5.
- (1/22) Gaussian process priors. [B] 6.4.1-6.4.5, 6.4.7.
- (1/25) K-means clustering; EM algorithm for Gaussian mixtures. [B] 9.1-9.2.
- (1/27) Principal components analysis. [B] 12.1.