Notes

See Syllabus for more information. You can find a list of week-by-week topics. Notes are not a substitute for going to lecture, as additional material may be covered in lecture. Notes are from a previous iteration of the course and may not be comprehensive. Refer to lectures.

• Note 1: Introduction
• Note 2: Linear Regression
• Note 3: Features, Hyperparameters, Validation
• Note 4: MLE and MAP for Regression (Part I)
• Note 5: Bias-Variance Tradeoff
• Note 6: Multivariate Gaussians
• Note 7: MLE and MAP for Regression (Part II)
• Note 8: Kernels, Kernel Ridge Regression
• Note 9: Total Least Squares
• Note 10: Principal Component Analysis (PCA)
• Note 11: Canonical Correlation Analysis (CCA)
• Note 12: Nonlinear Least Squares, Optimization
• Note 13: Gradient Descent Extensions
• Note 14: Neural Networks
• Note 15: Training Neural Networks
• Note 16: Discriminative vs. Generative Classification, LS-SVM
• Note 17: Logistic Regression
• Note 18: Gaussian Discriminant Analysis
• Note 19: Expectation-Maximization (EM) Algorithm, k-means Clustering
• Note 20: Support Vector Machines (SVM)
• Note 21: Generalization and Stability
• Note 22: Duality
• Note 23: Nearest Neighbor Classification
• Note 24: Sparsity
• Note 25: Decision Trees and Random Forests
• Note 26: Boosting
• Note 27: Convolutional Neural Networks (CNN)

Discussions

The discussion sections may cover new material and will give you additional practice solving problems. You can attend any discussion section you like. See Syllabus for more information.

Homeworks

All homeworks are fully graded. Your lowest homework score will be dropped, but this drop should be reserved for emergencies. See Syllabus for more information.