Some familiarity with complexity theory may be useful.
CPSC 421/501 may be may be useful background, but it is not required.
No background in statistics is required.
STAT 447B has some overlap with this course, and it may be useful background knowledge.
To determine whether your background is appropriate for this class, please look at the lecture notes here, here, here or here. This course will be quite similar. If you can follow those notes, then your background is a good fit for this course.
In exceptional circumstances, undergraduate students may also be admitted into the class.
Some topics we will cover include:
· PAC learning
· Sample complexity: VC dimension, Rademacher complexity
· Support vector machines, kernels
· Online learning: Perceptron, Winnow, Weighted majority, Regression
· Spectral partitioning and clustering
· Non-negative matrix factorization
The primary text is:
· Mohri, Rostamizadeh, Talwalkar Foundations of Machine Learning
Other books relevant to portions of the course include:
· Kearns, Vazirani An Introduction to Computational Learning Theory
· Cesa-Bianchi, Lugosi Prediction, Learning and Games
· Schapire, Freund Boosting: Foundations and Algorithms
· Devroye, Gyorfi, Lugosi A Probabilistic Theory of Pattern Recognition
· Shalev-Shwartz Online Learning and Online Convex Optimization
Around 4 problem-solving assignments and a final project.