| Date |
Topics |
Recommended Reading |
| 01/15 |
Overview and introduction to Machine Learning; Review of the foundational concepts from Probability, Optimization, and Information Theory
| slides |
| 01/20 |
Nearest Neighbor Classification and its theoretical foundations
| slides [MLaPP] 1.4.2 |
| 01/22 |
Linear Regression
| slides
[MLaPP] 1.4.5, 7.1-7.3, 7.5.1, 7.5.2, 7.5.4, 7.6, 1.4.7, 1.4.8
[ESL] 7.1, 7.2, 7.3, 7.10 |
| 01/27 |
Linear Regression with non-linear basis and overfitting
| slides [MLaPP] 7.1-7.3, 7.5.1, 7.5.2, 7.5.4, 7.6 |
| 01/29 |
Linear Classifiers and Surrogate Losses and Perceptrons
| slides
[MLaPP] 8.5.1-8.5.4, 1.4.6, 8.1-8.3
[ESL] 4.1-4.2, 4.4 |
| 02/03 |
Logistic Regression
| slides[MLaPP] 4.2.1 - 4.2.5, 1.4.6 |
| 02/05 |
Multi-class Classification
| slides
|
| 02/10 |
Linear Discriminant Analysis (LDA)
| slides [ESL] 4.3 |
| 02/12 |
Neural Networks
| slides [MLaPP] 16.5.1-16.5.6, [ESL] 11.3-11.7 |
| 02/17 |
Convolutional Neural Networks
| slides [MLaPP] 16.5.1-16.5.6, [ESL] 11.3-11.7
|
| 02/19 |
Kernel Methods
| slides [MLaPP] 14.1, 14.2.1-14.2.4, 14.4.1, 14.4.3
[ESL] 5.8, 6.3, 6.7 |
| 02/24 |
SVM I: SVM and its Primal Formulation
| slides
[MLaPP] 14.5.2-14.5.4
[ESL] 12.1-12.3 |
| 02/26 |
SVM II: Lagrangian Duality and SVM's Dual Formulation
| slides[MLaPP] 14.5.2-14.5.4
[ESL] 12.1-12.3 |
| 03/03 |
Quiz I
| |
| 03/05 |
Decision Trees
| slides[MLaPP] 16.2
[ESL] 16.3 |
| 03/10 |
Ensemble Methods: Boosting
| slides[MLaPP] 16.4.1-16.4.5, 16.4.8, 16.4.9 |
| 03/12 |
Wellness Day (No Class)
| |
| 03/17 |
Clustering: K-means
| slides[MLaPP] 3.5, 11.1-11.3, 11.4.1-11.4.4, 11.5
[ESL] 6.6.3, 8.5, 14.3.1-14.3.9 |
| 03/19 |
Gaussian Mixture Models
| slides [MLaPP] 3.5, 11.1-11.3, 11.4.1-11.4.4, 11.5
[ESL] 6.6.3, 8.5, 14.3.1-14.3.9 |
| 03/24 |
Density Estimation
| slides [MLaPP]
|
| 03/26 |
Naive Bayes
| slides [MLaPP] 10.1, 10.2.1-10.2.3, 10.3-10.5 |
| 03/31 |
Dimensionality Reduction and PCA
| slides [MLaPP] 12.2, [ESL] 14.5.1 |
| 04/02 |
Hidden Markov Models - I
| slides [MLaPP] 17.1-17.4, 17.5.1-17.5.2 |
| 04/07 |
Wellness Day (No Class)
| |
| 04/09 |
Hidden Markov Models - II
| slides [MLaPP] 17.1-17.4, 17.5.1-17.5.2 |
| 04/14 |
Multi-arm Bandit Models
| slides |
| 04/16 |
Reinforcement Learning
| slides |
| 04/21 |
Contemporary Topics in Machine Learning, Sequential Models and Autoencoders: Guest Lecture by Nitin Kamra Ph.D. Candidate, CS Dept., USC
| slides |
| 04/23 |
Algorithmic Fairness and the Law: Guest Lecture by Alice Xiang (Sony AI Research)
| slides |
| 04/28 |
Last Class: Summary and Project Logistics
| slides |
| 04/30 |
Wellness Day (No Class)
| |
| 05/03 |
Project Due
| |
| 05/10 8:00 - 10:00 AM PST |
Quiz 2 (Final)
| |
