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README.md

README.md

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