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EECS 349 Machine Learning Instructor: Doug Downey Note: slides adapted from Pedro Domingos, University of Washington, CSE 546 1

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Logistics Instructor: Doug Downey –Email: ddowney@eecs.northwestern.edu –Office: Ford 3-345 –Office hours: Wednesdays 3:00-4:30 (or by appt) TA: Francisco Iacobelli –Email: 'f-iacobelli@northwestern.edu‘ –Office: Ford 2-202 –Office Hours: Tuesdays 2:00-3:00 Web: www.eecs.northwestern.edu/~downey/courses/349/ 2

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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 3

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Source Materials T. Mitchell, Machine Learning, McGraw-Hill (Required) Papers 4

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Case Study: Farecast 5

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A Few Quotes “A breakthrough in machine learning would be worth ten 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) 6

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So What Is Machine Learning? Automating automation Getting computers to program themselves Writing software is the bottleneck Let the data do the work instead! 7

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Traditional Programming Machine Learning Computer Data Program Output Computer Data Output Program 8

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Magic? No, more like gardening Seeds = Algorithms Nutrients = Data Gardener = You Plants = Programs 9

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Sample Applications Web search Computational biology Finance E-commerce Space exploration Robotics Information extraction Social networks Debugging [Your favorite area] 10

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ML in a Nutshell Tens of thousands of machine learning algorithms Hundreds new every year Every machine learning algorithm has three components: –Representation –Evaluation –Optimization 11

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Representation Decision trees Sets of rules / Logic programs Instances Graphical models (Bayes/Markov nets) Neural networks Support vector machines Model ensembles Etc. 12

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Evaluation Accuracy Precision and recall Squared error Likelihood Posterior probability Cost / Utility Margin Entropy K-L divergence Etc. 13

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Optimization Combinatorial optimization –E.g.: Greedy search Convex optimization –E.g.: Gradient descent Constrained optimization –E.g.: Linear programming 14

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Types of Learning Supervised (inductive) learning –Training data includes desired outputs Unsupervised learning –Training data does not include desired outputs Semi-supervised learning –Training data includes a few desired outputs Reinforcement learning –Rewards from sequence of actions 15

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Inductive Learning Given examples of a function (X, F(X)) Predict function F(X) for new examples X –Discrete F(X): Classification –Continuous F(X): Regression –F(X) = Probability(X): Probability estimation 16

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What We’ll Cover Supervised learning –Decision tree induction –Rule induction –Instance-based learning –Neural networks –Support vector machines –Bayesian Learning –Learning theory Unsupervised learning –Clustering –Dimensionality reduction 17

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What You’ll Learn When can I use ML? –…and when is it doomed to failure? For a given problem, how do I: –Express as an ML task –Choose the right ML algorithm –Evaluate the results What are the unsolved problems/new frontiers? 18

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ML in Practice Understanding domain, prior knowledge, and goals Data integration, selection, cleaning, pre-processing, etc. Learning models Interpreting results Consolidating and deploying discovered knowledge Loop 19

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Mitchell, Chapters 1 & 2 Wired data mining article (linked on course Web page) (don’t take it too seriously) Reading for This Week 20

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