machine learning
Download
Skip this Video
Download Presentation
Machine Learning

Loading in 2 Seconds...

play fullscreen
1 / 35

Machine Learning - PowerPoint PPT Presentation


  • 219 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Machine Learning' - garry


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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
slide4
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

Then Profitable-Costumer ? = no

If Other-Delinquent-Account = 0

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

Then Profitable-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
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

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 board V(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?
ad