Syllabus is available here
| Lecture | Date | Content | Evaluation |
|---|---|---|---|
| 1 | 1/18/23 | Basics (I): Syllabus, Brief Review | |
| 2 | 1/23/23 | Basics (I): Syllabus, Brief Review, and Joint Distribution and Independence details | |
| 3 | 1/25/23 | Basics (I): Joint Distribution and Independence details | |
| 4 | 1/30/23 | Basics (I): Joint Distribution and Independence details | |
| 5 | 2/1/23 | Basics (I): Joint Distribution and Independence details | |
| 6 | 2/6/23 | Basics (II): Measures of Linear Relations and Their Applications details | |
| 7 | 2/8/23 | Basics (II): Measures of Linear Relations and Their Applications details | |
| 8 | 2/13/23 | Machine Learning (I): Unsupervised Learning, Covariance and Principal Component Analysis details | |
| 9 | 2/15/23 | Machine Learning (I): Unsupervised Learning, Covariance and Principal Component Analysis details | |
| 10 | 2/20/23 | Machine Learning (I): Classification and Conditional Distribution details | |
| 11 | 2/22/23 | Machine Learning (I): Classification and Conditional Distribution details | |
| 12 | 2/27/23 | Machine Learning (II): Makov Chain, Statistical Language Model and Conditional Distribution details | |
| 13 | 3/1/23 | Machine Learning (II): Makov Chain, Statistical Language Model and Conditional Distribution details | |
| 14 | 3/6/23 | Machine Learning (II): Makov Chain, Statistical Language Model and Conditional Distribution details | |
| 15 | 3/8/23 | Midterm | |
| 16 | 3/13/23 | Spring Break | |
| 17 | 3/15/23 | Spring Break | |
| 18 | 3/20/23 | Machine Learning (III): Bayes' Rule: Definition, Application, and Bayesian Estimation details | |
| 19 | 3/22/23 | Machine Learning (III): Bayes' Rule: Definition, Application, and Bayesian Estimation details | |
| 20 | 3/27/23 | Machine Learning (IV): Conditional Independence, Naïve Bayes Classifier and Other Applications details | |
| 21 | 3/29/23 | Machine Learning (IV): Conditional Independence, Naïve Bayes Classifier and Other Applications details | |
| 22 | 4/3/23 | Machine Learning (V): Conditional Expectation and Linear Regression details | |
| 23 | 4/5/23 | Machine Learning (V): Conditional Expectation and Linear Regression details | |
| 24 | 4/10/23 | Machine Learning (VI): Conditional Expectation, Nonparametric and Parametric Models details | |
| 25 | 4/12/23 | Machine Learning (VI): Conditional Expectation, Nonparametric and Parametric Models details | |
| 26 | 4/17/23 | Machine Learning (VI): Conditional Expectation, Nonparametric and Parametric Models details | |
| 27 | 4/19/23 | Machine Learning (VII): More Examples of Linear Regression details | |
| 28 | 4/24/23 | Machine Learning (VIII): High Dimension, Regularization and Lasso details | |
| 29 | 4/26/23 | Machine Learning (VIII): High Dimension, Regularization and Lasso details | |
| 30 | 5/1/23 | Causal Inference (I): Introduction to Causal Inference details | |
| 31 | 5/3/23 | Causal Inference (I): Introduction to Causal Inference details | |
| 32 | 5/8/23 | Causal Inference (II): Regression Discontinuity details | |
| 5/10/23 | Final |