UC Santa Cruz, April 22, 2009

Yahoo Labs, Santa Clara, CA

• Lecture 1: Introduction to Machine Learning and Probability Theory
  We introduce the concept of machine learning, as it is used to solve problems of pattern recognition, classification, regression, novelty detection and data cleaning. Subsequently we give a primer on probabilities, Bayes rule and inference (hypothesis testing for disease diagnosis).
• Lecture 2: Instance Based Learning We begin with a simple density estimator, Parzen Windows, which can be implemented very easily to perform estimation, as it requires essentially no algorithm to run before it can be used. A simple rule is given how to tune the parameters of the estimator, the Watson-Nadaraya Estimator for classification and regression, we discuss crossvalidation. Examples and applications conclude this lecture.
• Lecture 3: The Perceptron and Kernels A slightly more complex classifier is the Perceptron which produces linear separation of sets. We explain the algorithm, show its properties and implementation details. Subsequently we modify the algorithm to allow for nonlinear separation and multiclass discrimination. This leads us naturally to introduce kernels. Examples of kernels are given. We conclude with an overview of stochastic gradient descent algorithms.
• Assignments

Prerequisites

Nothing beyond undergraduate knowledge in mathematics is expected. More specifically, I assume:

• Basic linear algebra (matrix inverse, eigenvector, eigenvalue, etc.)
• Some numerical mathematics (beenficial but not required), such as matrix factorization, conditioning, etc.
• Basic statistics and probability theory (Normal distribution, conditional distributions).