Machine Learning: Introduction, Ridge Regression and Lasso
Course information
Overview and Syllabus
Schedule
Support
Assignments
Problem Set 1
Problem Set 2
Problem Set 3
Problem Set 4
Problem Set 5
Topics
I. Estimation Methods
Maximum Likelihood Lecture Notes
Binary Choice Models Lecture Notes
Generalized Method of Moments Lecture Notes
Discussion Review: MLE; Probit/Logit
Discussion Coding: MLE; Probit/Logit
II. Multinominal and Ordered Logits/Probits; Censoring and Selection models
Multinominal and Ordered Logits/Probits Lecture Notes
Censoring and Selection Models Lecture Notes
Discussion Review: GMM; Multinominal
Discussion Coding: GMM; Multinominal
III. Density Estimation and Semiparametric Econometrics
Nonparametric Density Estimation Lecture Notes
Nonparametric Regression Lecture Notes
Discussion Review: Censoring/Selection; Nonparametric
Discussion Coding: Censoring/Selection; Nonparametric
IV. Time Series Econometrics
Time Series Lecture Notes 1
Time Series Lecture Notes 2
Time Series Lecture Notes 3
Time Series Lecture Notes 4
Discussion Review: Time Series
Discussion Coding: Time Series
V. Instrumental Variables for Panel Data, Dynamic Panel Models
Lecture Notes
Discussion Review: Panel IV
Discussion Coding: Panel IV
VI. Machine Learning
Introduction, Ridge Regression and Lasso Lecture Notes
More on Lasso, Regression Trees and Random Forests Lecture Notes
Machine Learning: Introduction, Ridge Regression and Lasso
Lecture Notes
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