140.644.01
Statistical Machine Learning: Methods, Theory, and Applications

Prerequisite:

Students are expected to be familiar with the following topics to comfortably complete this class: Linear Algebra, Intermediate Statistics, and Basic R. If you do not know these topics, it is your responsibility to do background reading to make sure you understand these concepts.

Description:

Introduces statistical and computational foundations of modern statistical machine learning. Acquaints students with modern statistical machine learning models and their statistical and theoretical underpinnings. Topics covered include: regression and classification, resampling methods (cross-validation and bootstrap), model and variable selection, tree-based methods for regression and classification, functional regression models, unsupervised learning, support vector machines, ensemble methods, deep learning, visualization of large datasets. Example applications include cancer prognosis from microarray data, graphical models for data visualization, a prediction of survival using high-dimensional predictors.

Learning Objectives:

Upon successfully completing this course, students will be able to:

1. Identify the appropriate machine learning methods to address major scientific questions.
2. Interpret the results obtained by the common machine learning methods
3. Describe methods to evaluate and compare the performance of the machine learning models
4. Implement all analyses and methods within R

Methods of Assessment:

This course is evaluated as follows:

• 50% Homework
• 50% Final Project

Instructor Consent:

No consent required

Special Comments:

Please note: This is the in-person section of a course that is also offered virtually/online. Students will need to commit to the modality for which they register.