Foundations and Non-Parametric Methods
| Date | Lecture Topic | Instructor | Links |
|---|---|---|---|
| Mon Aug-31 | Intro, Three Axes of ML: Data, Algorithms, Tasks, Intro to probability | Ziv | Slides • Video |
| Wed Sep-02 | Bayesian Estimation, MAP, MLE | Ziv | Slides • Video |
| Fri Sep-04 | Recitation 1 | ||
| Mon Sep-07 | No Classes: Labor Day | ||
| Wed Sep-09 | Decision Theory, Risk Minimization, K nearest neighbors | Ziv | Slides • Video |
| Fri Sep-11 | Recitation 2 |
Prediction, Parametric Methods
| Date | Lecture Topic | Instructor | Links |
|---|---|---|---|
| Mon Sep-14 | Naive Bayes, Generative vs Discriminative | Ziv | Slides • Video |
| Wed Sep-16 | Decision Trees | Ziv | Slides • Video |
| Fri Sep-18 | Recitation 3 | ||
| Mon Sep-21 | Bagging, Random Forest, Linear regression | Ziv | Slides • Video |
| Wed Sep-23 | Logistic Regression | Ziv | Slides • Video |
| Fri Sep-25 | Recitation 4 | ||
| Mon Sep-28 | No Classes: Yom Kippur | ||
| Wed Sep-30 | Support Vector Machines 1 | Ziv | Slides • Video |
| Fri Oct-02 | Recitation 5 | ||
| Mon Oct-05 | Support Vector Machines 2 | Ziv | Slides • Video |
| Wed Oct-07 | Neural Networks and Deep Learning | Eric | Slides • Video |
| Fri Oct-09 | Recitation 6 | ||
| Mon Oct-12 | Neural Networks and Deep Learning 2 | Eric | Slides • Video |
| Wed Oct-14 | Boosting, Surrogate Losses, Ensemble Methods | Eric | Slides • Video |
| Fri Oct-16 | No Classes: Community Engagement day |
Unsupervised and Representation Learning
| Date | Lecture Topic | Instructor | Links |
|---|---|---|---|
| Mon Oct-19 | Clustering, Kmeans | Eric | Slides • Video |
| Wed Oct-21 | Clustering: Mixture of Gaussians, Expectation Maximization | Eric | Slides • Video |
| Fri Oct-23 | No Classes: Midsemester Break | ||
| Mon Oct-26 | Representation Learning: Feature Transformation, Random Features, PCA | Eric | Slides • Video |
| Wed Oct-28 | Representation Learning: PCA, ICA | Eric | Slides • Video |
| Fri Oct-30 | Recitation 7 |
Graphical and sequence models
| Date | Lecture Topic | Instructor | Links |
|---|---|---|---|
| Mon Nov-02 | Graphical Models (Bayesian Networks) | Ziv | Slides • Video |
| Wed Nov-04 | Graphical Models (Bayesian Networks 2) | Ziv | Slides • Video |
| Fri Nov-06 | Recitation 8 | ||
| Mon Nov-09 | Sequence Models: HMMs | Ziv | Slides • Video |
| Wed Nov-11 | Sequence Models: State Space Models, other time series models | Ziv | Slides • Video |
| Fri Nov-13 | Recitation 9 |
Theoretical considerations
| Date | Lecture Topic | Instructor | Links |
|---|---|---|---|
| Mon Nov-16 | Learning Theory: Statistical Guarantees for Empirical Risk Minimization | Eric | Slides • Video |
| Wed Nov-18 | Generalization, Model Selection | Eric | Slides • Video |
| Fri Nov-20 | Recitation 10 Proposed Final Exam Review | ||
| Mon Nov-23 | Exam | ||
| Wed Nov-25 | No Classes: Thanksgiving | ||
| Fri Nov-27 | No Classes: Thanksgiving |