Introduction to Machine Learning (415-0-20)

Instructors

Bradly Carson Stadie

Meeting Info

Technological Institute L251: Mon, Wed 12:30PM - 1:50PM

Overview of class

This class will be a modern introduction to machine learning methods, with a focus on prediction, clustering, and explainability. Topics include regression, random forests, deep learning, clustering, and unsupervised learning. The class will place an emphasis on practical implementations of such methods, and students will be expected to complete coding exercises involving real-world data sets.

Registration Requirements

Math 240-0, Math 230-2, and STAT 320-2 or statistics graduate standing

Learning Objectives

By the conclusion of the class, students will be able to read many modern papers in machine learning, in particular papers in deep learning and clustering. Students will be able to implement a wide variety of machine learning models on high-dimensional data sets. These models include lasso, ridge regression, random forests, feedforward networks, deep convolutional networks, recurrent networks, and k-means clustering.

Teaching Method

Lecture

Evaluation Method

Theory homework
Coding exercises
Presentations
In-class midterm exam

Class Materials (Required)

Computer with access to Python

Class Materials (Suggested)

Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, Jerome Friedman. Second Edition.