Course Material
Syllabus
Optimization Example
Bayesian Example 1
(JAGS code)
Bayesian Example 2
(data)
Scraping Example 1
Scraping Example 2
Topics Example 1
Topics Example 2
Supervised Learning Example 1
Supervised Learning Example 2
Nonparametrics Example 1
Nonparametrics Example 2
Ideal Point Estimation Example 1
Ideal Point Estimation Example 2
Ideal Point Estimation Example 3
Ideal Point Estimation Example 4
Ideal Point Estimation Example 5
Ideal Point Estimation Example 6
Neural Networks Example 1
Neural Networks Example 2
Neural Networks Example 3
Intro Slides
MLE Review Slides
Bayesian Slides
Text Analysis Slides
Machine Learning Slides
Nonparametrics Slides
Ideal Point Estimation Slides
Neural Networks Slides
Assignment 1
(data)
(jackman's appendix)
Assignment 2
(data)
Assignment 3
(data)
(data)
Python Notebook
Course Schedule
Class 1 (Aug. 31) - Intro, MLE Review
Class 2 (Sept. 7) - Bayesian Statistics
Class 3 (Sept. 14) - Bayesian Statistics
Class 4 (Sept. 21) - Bayesian Statistics, Assignment 1 Assigned
Class 5 (Sept. 28) - Text Analysis
Class 6 (Oct. 5) - Machine Learning
Class 7 (Oct. 12) - Machine Learning Assignment 1 Due, Assignment 2 Assigned
Class 8 (Oct. 19) - Machine Learning, Nonparametrics
Class 9 (Oct. 26) - Discussion of Grimmer, 2010 (Nicole) and Martin and Yurukoglu, 2017 (Sri), Assignment 2 Due
Class 10 (Nov. 2) - Nonparametrics
Class 11 (Nov. 9) - Ideal Point Estimation
Class 12 (Nov. 16) - Ideal Point Estimation, Neural Networks
Thanksgiving Break
Class 13 (Nov. 30) - Neural Networks
Class 14 (Dec. 7) - Discussion of Barbera, 2017 (Vitoria) and Burnham, 2023 (Ignacio)
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