Outline of Today CS4780 Machine Learning Fall 2009 Thorsten Joachims Cornell University Who we are What is learning Why should a computer be able to learn Examples of machine learning What it takes to build a learning system Syllabus Administrivia Pre Requisites Assignments Grading Textbook and course material Office Hours One Definition of Learning Definition Mitchell A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T as measured by P improves with experience E Textbook and Course Material Main Textbook Main Tom Mitchell Machine Learning McGraw Hill 1997 Cristianini Shawe Taylor Introduction to Support Vector Machines Cambridge University Press 2000 online Schoelkopf Smola Learning with Kernels MIT Press 2001 online Course pack one chapter Additional Reference optional Ethem Alpaydin Introduction to Machine Learning MIT Press 2004 Course 7otes Slides available on course homepage Material on blackboard Syllabus Concept Learning Hypothesis space version space target concept Instance Based Learning K nearest neighbor collaborative filtering Decision Trees TDIDT Representation bias vs search bias Hypothesis Tests Confidence intervals resampling estimates Linear Rules Perceptron Winnow Support Vector Machines Optimal hyperplane Kernels Generative Models Na ve Bayes MAP and Bayesian learning Hidden Markov Models Viterbi Expectation Maximization Complex Output Prediction natural language parsing Learning Theory PAC learning Mistake Bounds No Free Lunch Clustering HAC k means latent semantic indexing Pre Requisites and Related Courses Pre Requisites Programming skills e g CS 2110 Basic linear algebra e g MATH2940 Basic probability theory e g CS 2800 Related Courses CS4700 Foundations of Artificial Intelligence CS4300 Information Retrieval CS6780 Advanced Machine Learning CS678 Advanced Topics in Machine Learning CS6740 Advanced Language Technologies Assignments and Grading Deliverables 2 Prelim Exams 40 of Grade Final Project 15 of Grade Homeworks 5 assignments 40 of Grade Class Participation 5 of Grade Policies Assignments are due at the beginning of class on the due date Assignments turned in late will drop 5 points for each period of 24 hours for which the assignment is late No assignments will be accepted after the solutions have been made available Collaborations are not allowed except when explicitly permitted Must state all sources of material used in assignments or project Academic Integrity How to Get in Touch WWW Page http www cs cornell edu Courses cs4780 2009fa Email Addresses Thorsten Joachims tj cs cornell edu Mark Verheggen mark cs cornell edu Rick Ducott Haden Hooyeon Lee Vaibhav Goel Mailing list to all course staff cs4780 l lists cs cornell edu Office Hours Thorsten Joachims Tuesdays 4 30pm 5 30pm 4153 Upson Hall not 9 1 Other office hours TBD Final Project Organization Self defined topic related to your interests and research Groups of 3 4 students Each group has TA as advisor Deliverables Project proposal week after spring break Meetings with TA to discuss progress Short presentation in class last week of classes Project report exam period