Fall 2023 Quantitative Methods: Social Sciences GR5073 section 001


Call Number 13813
Day & Time
T 6:10pm-8:00pm
428 Pupin Laboratories
Points 3
Grading Mode Standard
Approvals Required None
Instructor Michael Parrott
Method of Instruction In-Person
Course Description

Prerequisites: basic probability and statistics, basic linear algebra, and calculus This course will provide a comprehensive overview of machine learning as it is applied in a number of domains. Comparisons and contrasts will be drawn between this machine learning approach and more traditional regression-based approaches used in the social sciences. Emphasis will also be placed on opportunities to synthesize these two approaches. The course will start with an introduction to Python, the scikit-learn package and GitHub. After that, there will be some discussion of data exploration, visualization in matplotlib, preprocessing, feature engineering, variable imputation, and feature selection. Supervised learning methods will be considered, including OLS models, linear models for classification, support vector machines, decision trees and random forests, and gradient boosting. Calibration, model evaluation and strategies for dealing with imbalanced datasets, n on-negative matrix factorization, and outlier detection will be considered next. This will be followed by unsupervised techniques: PCA, discriminant analysis, manifold learning, clustering, mixture models, cluster evaluation. Lastly, we will consider neural networks, convolutional neural networks for image classification and recurrent neural networks. This course will primarily us Python. Previous programming experience will be helpful but not requisite. Prerequisites: basic probability and statistics, basic linear algebra, and calculus.

Web Site Vergil
Department Quantitative Methods/Social Sciences
Enrollment 69 students (100 max) as of 4:05PM Wednesday, July 24, 2024
Subject Quantitative Methods: Social Sciences
Number GR5073
Section 001
Division Graduate School of Arts and Sciences
Section key 20233QMSS5073G001