Course Description

CIS 520 provides a fundamental introduction to the mathematics, algorithms and practice of machine learning. Topics covered include:

Supervised learning: least squares regression, logistic regression, perceptron, naive Bayes, support vector machines. Model and feature selection, ensemble methods, boosting. Learning theory: Bias/variance tradeoff. Online learning. Neural Nets/Deep Learning
Unsupervised learning: Clustering. K-means. EM. Mixture of Gaussians. PCA. More Deep Learning
Graphical models: HMMs, Bayesian and Markov networks.

Audience

The course is aimed broadly at advanced undergraduates and beginning graduate students in computer science, electrical engineering, mathematics, physics, and statistics. Undergraduates who meet the prerequisites are particularly encouraged to enroll, as are students from other departments. This is a hard course; A good alternative for those with less linear algebra or time is CIS419/519 or, if you want a really nice, much easier intro, take the Coursera ML course. If unsure which to take, see this.

Reading Materials

Semi-Optional Text: C. Bishop, Pattern Recognition and Machine Learning. 2007
Selected readings from other books and papers will be available on this web site.
See also our collected Resources

Software

We will be using Matlab for the course. We will provide “free” copies (included in your tuition) here .

Pre-requisites

Basic algorithms, data structures and complexity (dynamic programming, queues, stacks, graphs, big-O, P/NP). [CSE 320]
Basic probability and statistics (random variables, moments, standard distributions, simple regression). [CSE 261]
Basic linear algebra (matrices, vectors, norms, inverses).
Reasonable programming skills.

Evaluation

8 Problem Sets: 40%
Midterm: 20%
Project: 12%
Quizzes 3% - NOTE: quizzes are not graded — just marked as completed or not
Final: 25%

The problem sets include programming questions in Matlab. The midterm and final will be semi-closed book exams (cheat sheet allowed), which will encompass material covered in the lectures and assigned in the readings. For the project, you will be given an open-ended challenge problem, set up as a competition.

We do not take attendance, but you will learn more if you attend lectures instead of watching the recordings.

We try very hard to make questions unambiguous, but some ambiguities may remain. If you are confused, please ask on piazza or in office hours. If you feel you need to make assumption, please state your assumptions explicitly.