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Summer 2018 Quantitative Methods: Social Sciences GR5073 section 001
MACHINE LEARNING SOC SCI

Call Number 63146
Day & Time
Location
MW 4:00pm-6:10pm
405 International Affairs Building
Points 3
Approvals Required None
Instructor Michael Parrott
Type LECTURE
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 CourseWorks
Subterm Summ Sess (Q) 07/02 - 08/10
Department Graduate School of Arts and Sciences
Enrollment 8 students (40 max) as of 12:06AM Wednesday, May 23, 2018
Subject Quantitative Methods: Social Sciences
Number GR5073
Section 001
Division Graduate School of Arts and Sciences
Open To School of the Arts, Barnard, Columbia College, Engineering and Applied Science: Graduate, Graduate School of Arts and Science, General Studies, School of Professional Studies, Global Programs, International and Public Affairs
Campus Morningside
Note PREREQ: BASIC PROB&STATS,BASIC LINEAR ALGEBRA,BASIC CALCULUS
Section key 20182QMSS5073G001

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