Note: Some materials can be found on course website

Lecture1: Introduction to Machine Learning

Lecture2: Linear Regression

Lecture3: Linear Regression II

Lecture4: MLE for Linear Regression Logistic Regression

Lecture5: Second-Order Optimization and Softmax Regression

Lecture6: Overfitting and Regularization

Lecture7: Naive Bayes

Lecture8: Multinomial NB and KNN

Lecture9: Kernal

Lecture10: Kernal2

Lecture11: Support Vector Machine

Lecture12: Neural Networks

Lecture13: Backpropagation

Lecture14: Convolutional Neural Networks

Lecture15: Recurrent Neural Networks

Lecture17: Decision Trees Ensembles

Lecture18: K-Means Clustering

Lecture19: Gaussian Mixture Models

Lecture20: Hidden Markov Model

Lecture21: HMM and Dimensionality Reduction

Lecture22: PCA and Word Vectors

Lecture23: Multi-armed Bandits

Lecture24: Reinforcement Learning

Lecture25: Model-free Reinforcement Learning

Discussion1: Linear Algebra Calculus Probability Review

Discussion2: Python and Numpy

Discussion4: Cross Validation and Evaluation Metrics

Discussion5: Maxtrix Diagonalization and Symmetric Matrix

Discussion7: Pytorch