Lecture Notes from Prof. Anant Sahai's Iteration of the Course

• Note 2 – Linear Regression
• Note 3 – Feature Engineering
• Note 4 – MLE and MAP for Regression
• Note 5 – Bias-Variance Tradeoff
• Note 6 – Multivariate Gaussians
• Note 7 – MLE and MAP Part 2
• Note 8 – Kernels and Ridge Regression
• Note 9 – Total Least Squares
• Note 10 – PCA
• Note 11 – CCA
• Note 12 – Optimization
• Note 13 – Gradient Descent
• Note 14 – Neural Networks
• Note 15 – Training Neural Networks
• Note 16 – Generative vs. Discriminative Classification
• Note 18 – Gaussian Discriminant Analysis
• Note 19 – Clustering
• Note 20 – Support Vector Machines
• Note 21 – Generalization and Stability
• Note 22 – Duality
• Note 25 – Decision Trees
• Note 26 – Boosting
• Note 27 – Convolutional Neural Networks