![Sample Applications
• Web search
• Computational biology
• Finance
• E-commerce
• Space exploration
• Robotics
• Information extraction
• Social networks
• Debugging
• [Your favorite area]](https://image.slidesharecdn.com/intro-230920035459-33c832d2/85/intro-ppt-9-320.jpg)
intro.ppt
- 1. CSE 446 Machine Learning Instructor: Pedro Domingos
- 2. Logistics • Instructor: Pedro Domingos – Email: pedrod@cs – Office: CSE 648 – Office hours: Wednesdays 2:30-3:20 • TA: Hoifung Poon – Email: hoifung@cs – Office: 318 – Office hours: Mondays 1:30-2:20 • Web: www.cs.washington.edu/446 • Mailing list: cse446@cs
- 3. Evaluation • Four homeworks (15% each) – Handed out on weeks 1, 3, 5 and 7 – Due two weeks later – Some programming, some exercises • Final (40%)
- 4. Source Materials • R. Duda, P. Hart & D. Stork, Pattern Classification (2nd ed.), Wiley (Required) • T. Mitchell, Machine Learning, McGraw-Hill (Recommended) • Papers
- 5. 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. 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. Traditional Programming Machine Learning Computer Data Program Output Computer Data Output Program
- 8. Magic? No, more like gardening • Seeds = Algorithms • Nutrients = Data • Gardener = You • Plants = Programs
- 9. Sample Applications • Web search • Computational biology • Finance • E-commerce • Space exploration • Robotics • Information extraction • Social networks • Debugging • [Your favorite area]
- 10. 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. Representation • Decision trees • Sets of rules / Logic programs • Instances • Graphical models (Bayes/Markov nets) • Neural networks • Support vector machines • Model ensembles • Etc.
- 12. Evaluation • Accuracy • Precision and recall • Squared error • Likelihood • Posterior probability • Cost / Utility • Margin • Entropy • K-L divergence • Etc.
- 13. Optimization • Combinatorial optimization – E.g.: Greedy search • Convex optimization – E.g.: Gradient descent • Constrained optimization – E.g.: Linear programming
- 14. 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. 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. What We’ll Cover • Supervised learning – Decision tree induction – Rule induction – Instance-based learning – Bayesian learning – Neural networks – Support vector machines – Model ensembles – Learning theory • Unsupervised learning – Clustering – Dimensionality reduction
- 17. 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