Description

Comp 562 is an undergraduate level introduction to machine learning. Prior experience with basic linear algebra, probability, statistics, and programming is expected. A tentative list of topics includes: Probability Distributions; Maximum Likelihood Estimation; Regression; Classification; Cross Validation; Generalization; Optimization; Neural Networks; Nonparametric Estimators; Clustering; Autoencoders.

Background

Note: students enrolled should have a familiarity with basic linear algebra, probability, statistics, and programming. 

The following are refreshers for some of the prerequisite background.

• Brief Review of Basic Probability

• Calculus and Linear Algebra (source)

• Linear Algebra Review (list of videos)

• Math4ML 

Resources

While there are no required textbooks, the following are useful references.

• Introduction to Probability for Computing

• Machine Learning (Tom Mitchell)

• Pattern Recognition and Machine Learning

• The Elements of Statistical Learning

• Convex Optimization (B & V)

• Deep Learning