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Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

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Page 1: Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

Machine Learning Theory

Maria-Florina Balcan

Lecture 1, Jan. 12th 2010

Page 2: Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

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Image Classification

Document Categorization

Speech Recognition Branch Prediction

Protein Classification

Spam DetectionFraud Detection

Machine Learning

Playing Games Computational Advertising

Page 3: Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

• what kinds of tasks we can hope to learn, and from what kind of data

Goals of Machine Learning TheoryDevelop and analyze models to

understand:

• what types of guarantees might we hope to achieve

• prove guarantees for practically successful algs (when will they succeed, how long will they take?);

• Algorithms

Interesting connections to other areas including:

• Combinatorial Optimization

• Probability & Statistics • Game Theory

• Information Theory• Complexity Theory

• develop new algs that provably meet desired criteria

Page 4: Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

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Example: Supervised Classification

Goal: use emails seen so far to produce good prediction rule for future data.

Not spam spam

Decide which emails are spam and which are important.

Supervised classification

Page 5: Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

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example label

Reasonable RULES:

Predict SPAM if unknown AND (money OR pills)

Predict SPAM if 2money + 3pills –5 known > 0

Represent each message by features. (e.g., keywords, spelling, etc.)

Example: Supervised Classification

+

-

+++

--

--

-

Linearly separable

Page 6: Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

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Two Main Aspects of Supervised Learning

Algorithm Design. How to optimize?

Automatically generate rules that do well on observed data.

Confidence Bounds, Generalization Guarantees, Sample Complexity

Confidence for rule effectiveness on future data.

Well understood for passive supervised learning.

Page 7: Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

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• Semi-Supervised Learning

Using cheap unlabeled data in addition to labeled data.

• Active Learning

The algorithm interactively asks for labels of informative examples.

Other Protocols for Supervised Learning

Theoretical understanding severely lacking until a couple of years ago. Lots of progress recently. We will cover some of these.

• Learning with Membership Queries

• Statistical Query Learning

Page 8: Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

Structure of the Class

• Simple algos and hardness results for supervised learning.

• Classic, state of the art algorithms: AdaBoost and SVM.

• Basic models for supervised learning: PAC and SLT.

• Standard Sample Complexity Results (VC dimension)• Weak-learning vs. Strong-learning

Page 9: Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

Structure of the Class

• Classification noise and the Statistical-Query model

• Incorporating Unlabeled Data in the Learning Process.

• Reinforcement Learning

• Learning Real Valued Functions

• Modern Sample Complexity Results• Rademacher Complexity

• Margin analysis of Boosting and SVM

• Incorporating Interaction in the Learning Process: • Active Learning• Learning with Membership Queries

Page 10: Machine Learning Theory Maria-Florina Balcan Lecture 1, Jan. 12 th 2010

Admin

• Course web page:

• 6 hwk assignments. Exercises/problems (pencil-and-paper problem-solving variety).

• Small project: explore a theoretical question, try some experiments, or read a paper and explain the idea. Short writeup and possibly presentation. Small groups ok.

• Take-home exam.

http://www.cc.gatech.edu/~ninamf/ML10/

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