3 Cr. (Hrs.:3 Lec.)
Introduction to the framework of learning from examples, various learning algorithms such as neural networks, and generic learning principles such as inductive bias, Occam's Razor, and data mining. Reviews some statistical learning techniques, but focus is on non-statistical techniques. (2nd)
Course generally offered spring (2nd) semester.
R1. Explain a range of machine learning fundamentals including:
o Supervised and unsupervised learning
o Reinforcement Learning
R2. Explain and solve problems using the basic models and methods:
o Decision Trees
o Bayesian Network
o Simulated Annealing
o Genetic Algorithms
o Support Vector Machines
o Neural Networks
R3. Discuss basic Machine Learning concepts such as algorithms, heuristics, solutions spaces, and relate them to brute force searching;
R4. Develop workable representations of knowledge for the various approaches, and be able to identify situations in which scaling and information density will become issues.