CS194-10 Fall 2011 Introduction to Machine
Course outline Overview of machine learning (today) Classical supervised learning Linear regression, perceptrons, neural nets, SVMs, decision trees, nearest neighbors Learning probabilistic models Probabilistic classifiers (logistic regression, etc.) Unsupervised learning, density estimation, EM Bayes net learning Time series models Lecture 1 8/25/11 CS 194-10 Fall 2011, Stuart Russell Learning is…. … a computational process for improving performance based on experience Lecture 1 8/25/11 CS 194-10 Fall 2011, Stuart Russell