In plain words: Free lecture notes that teach machine learning from three angles: how to update beliefs from data, how to find patterns in numbers, and how to tell when a learner will work on new data. They gather the standard course topics in one place for students.

Abstract

Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).

Amnon Shashua
arXiv:0904.3664 · cs.LG · submitted Apr 23, 2009
abstract · pdf · html · 109 pages, class notes of Machine Learning course given at the Hebrew University of Jerusalem