Eecs 349 machine learning
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EECS 349 Machine Learning. Instructor: Doug Downey Note: slides adapted from Pedro Domingos, University of Washington, CSE 546. Logistics. Instructor: Doug Downey Email: [email protected] Office: Ford 3-345 Office hours: Wednesdays 3:00-4:30 (or by appt)

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EECS 349 Machine Learning

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Eecs 349 machine learning

EECS 349Machine Learning

Instructor: Doug Downey

Note: slides adapted from Pedro Domingos, University of Washington, CSE 546


Logistics

Logistics

  • Instructor: Doug Downey

    • Email: [email protected]

    • Office: Ford 3-345

    • Office hours: Wednesdays 3:00-4:30 (or by appt)

  • TA: Francisco Iacobelli

    • Email: [email protected]

    • Office: Ford 2-202

    • Office Hours: Tuesdays 2:00-3:00

  • Web:www.eecs.northwestern.edu/~downey/courses/349/


Evaluation

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

Source Materials

  • T. Mitchell, Machine Learning,McGraw-Hill (Required)

  • Papers


Case study farecast

Case Study: Farecast


A few quotes

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

So What Is Machine Learning?

  • Automating automation

  • Getting computers to program themselves

  • Writing software is the bottleneck

  • Let the data do the work instead!


Eecs 349 machine learning

Traditional Programming

Machine Learning

Computer

Data

Output

Program

Computer

Data

Program

Output


Magic

Magic?

No, more like gardening

  • Seeds = Algorithms

  • Nutrients = Data

  • Gardener = You

  • Plants = Programs


Sample applications

Sample Applications

  • Web search

  • Computational biology

  • Finance

  • E-commerce

  • Space exploration

  • Robotics

  • Information extraction

  • Social networks

  • Debugging

  • [Your favorite area]


Ml in a nutshell

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

Representation

  • Decision trees

  • Sets of rules / Logic programs

  • Instances

  • Graphical models (Bayes/Markov nets)

  • Neural networks

  • Support vector machines

  • Model ensembles

  • Etc.


Evaluation1

Evaluation

  • Accuracy

  • Precision and recall

  • Squared error

  • Likelihood

  • Posterior probability

  • Cost / Utility

  • Margin

  • Entropy

  • K-L divergence

  • Etc.


Optimization

Optimization

  • Combinatorial optimization

    • E.g.: Greedy search

  • Convex optimization

    • E.g.: Gradient descent

  • Constrained optimization

    • E.g.: Linear programming


Types of learning

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


Inductive learning

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


What we ll cover

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


What you ll learn

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?


Ml in practice

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


Reading for this week

Reading for This Week

  • Mitchell, Chapters 1 & 2

  • Wired data mining article (linked on course Web page)(don’t take it too seriously)


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