CSE 474/574: Introduction to Machine Learning

Machine learning is concerned with the question of how to make computers learn from experience. The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems. For example, machine learning techniques are used to create spam filters, analyze customer purchase data, understand natural language, or detect fraudulent credit card transactions.

This course will introduce the fundamental set of techniques and algorithms that constitute machine learning as of today, ranging from classification methods like decision trees, support vector machines and neural networks, over-structured models like hidden Markov models, to clustering and matrix factorization methods for recommendation. The course will not only discuss individual algorithms and methods but also tie principles and approaches together from a theoretical perspective, as well as the gaining fundamentals of applying machine learning techniques to real-world problems. In particular, the course will cover the following topics:

Generative Models, Bayesian Learning, Linear Regression, Logistic Regression, Perceptron, Neural Networks, Convolutional Neural Network, Recurrent Neural Network, Reinforcement Learning, Graphical Models, Clustering, Latent Linear Models, Support Vector Machines, Decision Tree, Boosting, Random Forest, Hidden Markov Models.