It will emphasize practice over mathematical theory, and students will spend a considerable amount of class time gaining experience with each algorithm using existing packages in R, Python, and Linux libraries. The course will cover the following topics: regression and logistic regression, regularized regression including the lasso and elastic net techniques, support vector machines, neural networks, decision trees, boosted decision trees and random forests, online learning, k-means and special clustering, and survival analysis.
Prerequisites:
- MSCA 31008: Data Mining Principles
- MSCA 31010: Linear & Non-Linear Models
- MSCA 37014: Python for Analytics
