UNIVERSITY OF MICHIGAN STATISTICAL LEARNING WORKSHOP

Rackham Interdisciplinary Workshop

Winter 2019 Schedule

January 16: Discussion of "A Multinomial Framework for Ideal Point Estimation" by Max Goplerud (Political Analysis 27, 2019)
February 6: Kevin McAlister, "A Beta Process Approach to Estimating Dimensionality"
February 13: Kevin McAlister, Hwayong Shin, and Erin Cikanek, "Ordered Bayesian Aldrich-McKelvey Scaling: Improving Bias Correction on the Liberal-Conservative Scale"
February 20: Discussion of "Hierarchical Item Response Models for Analyzing Public Opinion" by Xiang Zhou (Political Analysis 2019)
March 13: Erin Cikanek, "Classifying Emotional Intensity in Text"
March 14: Presentation of recent work on text analysis by Sarah Bouchat (Northwestern University) (joint event with CQPS)
March 20: Tutorial on Word2vec by Patrick Wu
March 27: Research design on detecting innovation and diffusion in text data by Mike Thompson-Brusstar
April 10: Patrick Wu, "Geometry of Partisanship"
April 18: Culminating Event: Gary King, "How to Measure Legislative District Compactness If You Only Know It When You See It" (https://gking.harvard.edu/presentations/how-measure-legislative-district-compactness-if-you-only-know-it-when-you-see-it-4)

October 11: Diogo Ferrari Practice Job Talk
October 25: Kevin McAlister Practice Job Talk
November 1: Discussion of "Text Preprocessing for Unsupervised Learning: Why It Matters, When It Misleads, and What To Do About It" by Matthew J. Denny and Arthur Spirling (Political Analysis 26, 2018)
November 8: Discussion of "Inferring Roll-Call Scores from Campaign Contributions Using Supervised Machine Learning" by Adam Bonica (American Journal of Political Science 62, 2019)
November 15: Five-Minute Fiesta
November 22: Thanksgiving Break, NO MEETING
November 29: Discussion of "Community Detection Using Spectral Clustering on Sparse Geosocial Data” by Yves van Gennip et al. (2012)
December 6: No Meeting

The most helpful book to help read papers and understand presentations in this workshop will be Elements of Statistical Learning (ESL) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, supplemented with "Machine Learning: a Probabilistic Perspective," by Kevin Patrick Murphy, and "Deep Learning" by Goodfellow and Yoshua Bengio and Aaron Courville. For those new to statistical learning, an introductory text, Introduction to Statistical Learning (ISL) can be found for free here. In addition, lecture videos corresponding to each chapter in the book can be found here. Other material used in the workshop may vary from week to week. They are described in the schedule.