introppt整理.ppt

EECS 349Machine Learning Instructor: Doug Downey Note: slides adapted from Pedro Domingos, University of Washington, CSE 546 * Logistics Instructor: Doug Downey Email: ddowney@ Office: Ford 3-345 Office hours: Wednesdays 3:00-4:30 (or by appt) TA: Francisco Iacobelli Email: f-iacobelli@‘ Office: Ford 2-202 Office Hours: Tuesdays 2:00-3:00 Web: /~downey/courses/349/ * Evaluation Three homeworks (50% of grade) Assigned Friday of weeks 1, 3, and 5 Due two weeks later Via e-mail at 5:00PM Thursday Late assignments will not be graded (!) Some programming, some exercises Final Project (50%) Teams of 2 or 3 Proposal due Thursday, October 16th * Source Materials T. Mitchell, Machine Learning,McGraw-Hill (Required) Papers * Case Study: Farecast * A Few Quotes “A breakthrough in machine learning would be worthten Microsofts” (Bill Gates, Chairman, Microsoft) “Machine learning is the next Internet” (Tony Tether, Director, DARPA) “Machine learning is the hot new thing” (John Hennessy, President, Stanford) “Web rankings today are mostly a matter of machine learning” (Prabhakar Raghavan, Dir. Research, Yahoo) “Machine learning is going to result in a real revolution” (Greg Papadopoulos, CTO, Sun) “Machine learning is today’s discontinuity” (Jerry Yang, CEO, Yahoo) * So What Is Machine Learning? Automating automation Getting computers to program themselves Writing software is the bottleneck Let the data do the work instead! * Traditional Programming Machine Learning Computer Data Program Output Computer Data Output Program * Magic? No, more like gardening Seeds = Algorithms Nutrients = Data Gardener = You Plants = Programs * Sample Applications Web search Computational biology Finance E-commerce Space exploration Robotics Information extraction Social networks Debugging [Your favorite area] * ML in a Nutshell Tens of thousands of machine learning algorithms Hundreds new every year Every machine learning algorithm has three components: Representation Evaluati