1. CS491/591 Introduction to Machine Learning Fall 2004 Terran Lane terran@cs.unm.edu

2. Today: Syllabus Q & A Machine Learning: the “big picture”

3. Next time: Pre-test (ungraded) -- ~20 min Mostly to help me understand you Might brush up on linear algebra, basic probability, etc. Background and first learning algorithm

4. Syllabus: the crunchy stuff Textbook Resources Web page: ~terran/classes/cs591-f04 / Mailing list: ml-class@cs.unm.edu Me: terran@cs.unm.edu : Office hours: W, 9:00-11:00 AM; FEC345B Assignments/grading Homeworks Readings Exams Final Project: undergrad & grad Be on time Don’t cheat

5. What is Machine Learning? (what is learning at all?)

6. Topics in ML Supervised learning: learning to identify and predict Classification/Concept learning Regression Unsupervised learning: learning to group and describe Clustering Descriptive modeling Reinforcement learning: learning to act and explore Markov decision processes Partial observability