Machine learning
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Machine Learning. Spring 2010 Rong Jin. CSE847 Machine Learning. Instructor: Rong Jin Office Hour: Tuesday 4:00pm-5:00pm Thursday 4:00pm-5:00pm Textbook Machine Learning The Elements of Statistical Learning Pattern Recognition and Machine Learning Many subjects are from papers

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Machine learning

Machine Learning

Spring 2010

Rong Jin


Cse847 machine learning

CSE847 Machine Learning

  • Instructor: Rong Jin

  • Office Hour:

    • Tuesday 4:00pm-5:00pm

    • Thursday 4:00pm-5:00pm

  • Textbook

    • Machine Learning

    • The Elements of Statistical Learning

    • Pattern Recognition and Machine Learning

    • Many subjects are from papers

  • Web site: http://www.cse.msu.edu/~cse847


Requirements

Requirements

  • ~10 homework assignments

  • Course project

    • Topic: visual object recognition

    • Data: over one million images with extracted visual features

    • Objective: build a classifier that automatically identify the class of objects in images

  • Midterm exam & final exam


Machine learning

Goal

  • Familiarize you with the state-of-art in Machine Learning

    • Breadth: many different techniques

    • Depth: Project

    • Hands-on experience

  • Develop the way of machine learning thinking

    • Learn how to model real-world problems by machine learning techniques

    • Learn how to deal with practical issues


Course outline

Course Outline

  • Theoretical Aspects

  • Information Theory

  • Optimization Theory

  • Probability Theory

  • Learning Theory

  • Practical Aspects

  • Supervised Learning Algorithms

  • Unsupervised Learning Algorithms

  • Important Practical Issues

  • Applications


Today s topics

Today’s Topics

  • Why is machine learning?

  • Example: learning to play backgammon

  • General issues in machine learning


Why machine learning

Why Machine Learning?

  • Past: most computer programs are mainly made by hand

  • Future: Computers should be able to program themselves by the interaction with their environment


Recent trends

Recent Trends

  • Recent progress in algorithm and theory

  • Growing flood of online data

  • Computational power is available

  • Growing industry


Three niches for machine learning

Three Niches for Machine Learning

  • Data mining: using historical data to improve decisions

    • Medical records  medical knowledge

  • Software applications that are difficult to program by hand

    • Autonomous driving

    • Image Classification

  • User modeling

    • Automatic recommender systems


Typical data mining task

Typical Data Mining Task

  • Given:

    • 9147 patient records, each describing pregnancy and birth

    • Each patient contains 215 features

  • Task:

    • Classes of future patients at high risk for Emergency Cesarean Section


Data mining results

Data Mining Results

One of 18 learned rules:

If no previous vaginal delivery

abnormal 2nd Trimester Ultrasound

Malpresentation at admission

Then probability of Emergency C-Section is 0.6


Credit risk analysis

Credit Risk Analysis

Learned Rules:

If Other-Delinquent-Account > 2

Number-Delinquent-Billing-Cycles > 1

ThenProfitable-Costumer ? = no

IfOther-Delinquent-Account = 0

(Income > $30K or Years-of-Credit > 3)

ThenProfitable-Costumer ? = yes


Programs too difficult to program by hand

Programs too Difficult to Program By Hand

  • ALVINN drives 70mph on highways


Programs too difficult to program by hand1

Programs too Difficult to Program By Hand

  • ALVINN drives 70mph on highways


Programs too difficult to program by hand2

Programs too Difficult to Program By Hand

  • Visual object recognition


Image retrieval using texts

Image Retrieval using Texts


Software that models users

What to Recommend?

Description: A high-school boy is given the chance to write a story about an up-and-coming rock band as he accompanies it on their concert tour.

Recommend: ?

Description:A homicide detective and a fire marshall must stop a pair of murderers who commit videotaped crimes to become media darlings

Rating:

Description: A biography of sports legend, Muhammad Ali, from his early days to his days in the ring

Rating:

No

Description: A young adventurer named Milo Thatch joins an intrepid group of explorers to find the mysterious lost continent of Atlantis.

Recommend: ?

Description:Benjamin Martin is drawn into the American revolutionary war against his will when a brutal British commander kills his son.

Rating:

Yes

Software that Models Users

History


Netflix contest

Netflix Contest


Relevant disciplines

Relevant Disciplines

  • Artificial Intelligence

  • Statistics (particularly Bayesian Stat.)

  • Computational complexity theory

  • Information theory

  • Optimization theory

  • Philosophy

  • Psychology


Today s topics1

Today’s Topics

  • Why is machine learning?

  • Example: learning to play backgammon

  • General issues in machine learning


What is the learning problem

What is the Learning Problem

  • Learning = Improving with experience at some task

    • Improve over task T

    • With respect to performance measure P

    • Based on experience E

  • Example: Learning to Play Backgammon

    • T: Play backgammon

    • P: % of games won in world tournament

    • E: opportunity to play against itself


Backgammon

Backgammon

  • More than 1020 states (boards)

  • Best human players see only small fraction of all board during lifetime

  • Searching is hard because of dice (branching factor > 100)


Td gammon by tesauro 1995

TD-Gammon by Tesauro (1995)

  • Trained by playing with itself

  • Now approximately equal to the best human player


Learn to play chess

Learn to Play Chess

  • Task T: Play chess

  • Performance P: Percent of games won in the world tournament

  • Experience E:

    • What experience?

    • How shall it be represented?

    • What exactly should be learned?

    • What specific algorithm to learn it?


Choose a target function

Choose a Target Function

  • Goal:

    • Policy: : b  m

  • Choice of value function

    • V: b, m  

B = board

 = real values


Choose a target function1

Choose a Target Function

  • Goal:

    • Policy: : b  m

  • Choice of value function

    • V: b, m  

    • V: b  

B = board

 = real values


Value function v b example definition

Value Function V(b): Example Definition

  • If b final board that is won: V(b) = 1

  • If b final board that is lost: V(b) = -1

  • If b not final boardV(b) = E[V(b*)] where b* is final board after playing optimally


Representation of target function v b

Representation of Target Function V(b)

Same value

for each board

Lookup table

(one entry for each board)

  • Summarize experience into

  • Polynomials

  • Neural Networks

No Learning

No Generalization


Example linear feature representation

Example: Linear Feature Representation

  • Features:

    • pb(b), pw(b) = number of black (white) pieces on board b

    • ub(b), ub(b) = number of unprotected pieces

    • tb(b), tb(b) = number of pieces threatened by opponent

  • Linear function:

    • V(b) = w0pb(b)+ w1pw(b)+ w2ub(b)+ w3uw(b)+ w4tb(b)+ w5tw(b)

  • Learning:

    • Estimation of parameters w0, …, w5


Tuning weights

Tuning Weights

  • Given:

    • board b

    • Predicted value V(b)

    • Desired value V*(b)

  • Calculate

    error(b) = (V*(b) – V(b))2

    For each board feature fi

    wi wi + cerror(b)fi

  • Stochastically minimizes

    b (V*(b)-V(b))2

Gradient Descent Optimization


Obtain boards

Obtain Boards

  • Random boards

  • Beginner plays

  • Professionals plays


Obtain target values

Obtain Target Values

  • Person provides value V(b)

  • Play until termination. If outcome is

    • Win: V(b)  1 for all boards

    • Loss: V(b)  -1 for all boards

    • Draw: V(b)  0 for all boards

  • Play one move: b  b’

    V(b)  V(b’)

  • Play n moves: b  b’… b(n)

    • V(b)  V(b(n))


A general framework

MathematicalModeling

Finding Optimal Parameters

+

Statistics

Optimization

Machine Learning

A General Framework


Today s topics2

Today’s Topics

  • Why is machine learning?

  • Example: learning to play backgammon

  • General issues in machine learning


Importants issues in machine learning

Importants Issues in Machine Learning

  • Obtaining experience

    • How to obtain experience?

      • Supervised learning vs. Unsupervised learning

    • How many examples are enough?

      • PAC learning theory

  • Learning algorithms

    • What algorithm can approximate function well, when?

    • How does the complexity of learning algorithms impact the learning accuracy?

    • Whether the target function is learnable?

  • Representing inputs

    • How to represent the inputs?

    • How to remove the irrelevant information from the input representation?

    • How to reduce the redundancy of the input representation?


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