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Managerial Economics II: Machine Learning and Causal Inference
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lecture Update different visualization for joint dist, and continuous variables Jan 11, 2020
summary First week Jan 10, 2020
syllabus syllabus Jan 9, 2020
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2020s.Rproj syllabus Jan 9, 2020
README.md Summary pages Jan 9, 2020

README.md

Econ 5043: Machine Learning and Causal Inference

Syllabus

Syllabus is available here

Detailed Schedule (subject to change depending on our progress)

Lecture Date Content Evaluation
1 1/13/20 Basics (I): Syllabus, Brief Review, and Joint Distribution and Independence details
2 1/15/20 Basics (I): Joint Distribution and Independence details
3 1/20/20 MLK Day (No Class)
4 1/22/20 Basics (I): Joint Distribution and Independence details PS1 and Titanic Data ; Quiz 1
5 1/27/20 Basics (II): Measures of Linear Relations and Their Applications details
6 1/29/20 Basics (II): Measures of Linear Relations and Their Applications details PS2, Wage Data and Forecast Data ; Quiz 2
7 2/3/20 Machine Learning (I): Classification and Conditional Distribution details
8 2/5/20 Machine Learning (I): Classification and Conditional Distribution details PS3; Quiz 3
9 2/10/20 Machine Learning (II): Makov Chain, Statistical Language Model and Conditional Distribution details
10 2/12/20 Machine Learning (II): Makov Chain, Statistical Language Model and Conditional Distribution details
11 2/17/20 Machine Learning (II): Makov Chain, Statistical Language Model and Conditional Distribution details PS4; Quiz 3
12 2/19/20 Machine Learning (III): Bayes' Rule: Definition, Application, and Bayesian Estimation details
13 2/24/20 Machine Learning (III): Bayes' Rule: Definition, Application, and Bayesian Estimation details PS5; Quiz 3
14 2/26/20 Machine Learning (IV): Conditional Independence, Naïve Bayes Classifier and Other Applications details
15 3/2/20 Machine Learning (IV): Conditional Independence, Naïve Bayes Classifier and Other Applications details PS6; Quiz 4
16 3/4/20 Machine Learning (IV): Conditional Independence, Naïve Bayes Classifier and Other Applications details
17 3/9/20 Machine Learning (V): Conditional Expectation and Linear Regression details
18 3/11/20 Midterm
19 3/16/20 Spring Break
20 3/18/20 Spring Break
21 3/23/20 Machine Learning (V): Conditional Expectation and Linear Regression details PS7
22 3/25/20 Machine Learning (VI): Conditional Expectation, Nonparametric and Parametric Models details PS8; Quiz 5
23 3/30/20 Machine Learning (VI): Conditional Expectation, Nonparametric and Parametric Models details
24 4/1/20 Machine Learning (VI): Conditional Expectation, Nonparametric and Parametric Models details PS8
25 4/6/20 Machine Learning (VII): More Examples of Linear Regression details PS9
26 4/8/20 Machine Learning (VIII): High Dimension, Regularization and Lasso details
27 4/13/20 Machine Learning (VIII): High Dimension, Regularization and Lasso details
28 4/15/20 Causal Inference (I): Introduction to Causal Inference details
29 4/20/20 Causal Inference (I): Introduction to Causal Inference details
30 4/22/20 Causal Inference (I): Introduction to Causal Inference details
31 4/27/20 Causal Inference (II): Subclassification, Matching, and Linear Regression details
32 4/29/20 Causal Inference (III): Regression Discontinuity details
5/7/20 Final
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