Machine Learning Course in CSE 446 at University of Washington, Assignments of Machine Learning

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CSE 446

Machine Learning

Instructor: Pedro Domingos

Logistics

• (^) Instructor: Pedro Domingos
  • (^) Email: pedrod@cs
  • (^) Office: CSE 648
  • (^) Office hours: Wednesdays 2:30-3:
• (^) TA: Hoifung Poon
  • (^) Email: hoifung@cs
  • (^) Office: 318
  • (^) Office hours: Mondays 1:30-2:
• (^) Web: www.cs.washington.edu/
• (^) Mailing list : cse446@cs

Source Materials

• (^) R. Duda, P. Hart & D. Stork, Pattern

Classification (

nd

ed.), Wiley (Required)

• (^) T. Mitchell, Machine Learning ,

McGraw-Hill (Recommended)

• (^) Papers

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)

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

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

Representation

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

Optimization

• (^) Combinatorial optimization
  • (^) E.g.: Greedy search
• (^) Convex optimization
  • (^) E.g.: Gradient descent
• (^) Constrained optimization
  • (^) E.g.: Linear programming

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

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

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