* Below is a collection of topics, of which we plan to cover a large subset this quarter. The specific topics and the order is subject to change.

Category	Topic
Review	Linear Algebra Matrix Calculus Probability and Statistics
Supervised Learning	Linear Regression (Gradient Descent, Normal Equations) Weighted Linear Regression (LWR) Logistic Regression, Perceptron Newton's Method, KL-divergence, (cross-)Entropy, Natural Gradient Exponential Family and Generalized Linear Models Generative Models (Gaussian Discriminant Analysis, Naive Bayes) Kernel Method (SVM, Gaussian Processes) Tree Ensembles (Decision trees, Random Forests, Boosting and Gradient Boosting)
Learning Theory	Regularization Bias-Variance Decomposition and Tradeoff Concentration Inequalities Generalization and Uniform Convergence VC-dimension
Deep Learning	Neural Networks Backpropagation Deep Architectures
Unsupervised Learning	K-means Gaussian Mixture Model (GMM) Expectation Maximization (EM) Variational Auto-encoder (VAE) Factor Analysis Principal Components Analysis (PCA) Independent Components Analysis (ICA)
Reinforcement Learning (RL)	Markov Decision Processes (MDP) Bellmans Equations Value Iteration and Policy Iteration Value Function Approximation Q-Learning
Application	Advice on structuring an ML project Evaluation Metrics