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Seminar on Machine Learning Rada Mihalcea. Introduction to Machine Learning Administrivia January 13(!), 2004. Machine Learning?. Definition:

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seminar on machine learning rada mihalcea

Seminar on Machine LearningRada Mihalcea

Introduction to Machine Learning

Administrivia

January 13(!), 2004

machine learning
Machine Learning?

Definition:

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience.

  • Machine Learning has to do with designing computer programs that improve their performance through experience
disciplines relevant to ml
Disciplines relevant to ML
  • Artificial intelligence
  • Control theory
  • Information theory
  • Computational complexity theory
  • Psychology and neurobiology
  • Statistics
applications of ml
Applications of ML
  • Learning to recognize spoken words
    • SPHINX (Lee 1989)
  • Learning to drive an autonomous vehicle
    • ALVINN (Pomerleau 1989)
  • Learning to classify celestial objects
    • (Fayyad et al 1995)
  • Learning to play world-class backgammon
    • TD-GAMMON (Tesauro 1992)
  • Learning to translate between languages
  • Learning to classify texts into categories
    • Web directories
main directions in ml
Main directions in ML
  • Data mining
    • Finding patterns in data
    • Use “historical” data to make a decision
      • Predict weather based on current conditions
  • Self customization
    • Automatic feedback integration
    • Adapt to user “behaviour”
      • Recommending systems
  • Writing applications that cannot be programmed by hand
    • In particular because they involve huge amounts of data
      • Speech recognition
      • Hand writing recognition
      • Text understanding
learning problem an example
Learning Problem (an example)

Learning: improving with experience at some task

  • Improve over task T
  • With respect to performance measure P
  • Based on experience E

Example: Learn to play checkers:

  • T: play checkers
  • P: percentage of games won in a tournament
  • E: opportunity to play against itself
learning to play checkers
Learning to play checkers
  • T: play checkers
  • P: percentage of games won
  • What experience?
  • What exactly should be learned?
  • How shall it be represented?
  • What specific algorithm to learn it?
type of training experience
Type of Training Experience
  • Direct or indirect?
    • Direct: board state  correct move
    • Indirect: outcome of a complete game
    • Credit assignment problem
  • Teacher or not ?
    • Teacher selects board states
    • Learner can select board states
  • Is training experience representative of performance goal?
    • Training playing against itself
    • Performance evaluated playing against world champion
choose target function
Choose Target Function
  • ChooseMove : B  M : board state  move
    • Maps a legal board state to a legal move
  • Evaluate : BV : board state  board value
    • Assigns a numerical score to any given board state, such that better board states obtain a higher score
    • Select the best move by evaluating all successor states of legal moves and pick the one with the maximal score
possible definition of target function
Possible Definition of Target Function
  • If b is a final board state that is won then V(b) = 100
  • If b is a final board state that is lost then V(b) = -100
  • If b is a final board state that is drawn then V(b)=0
  • If b is not a final board state, then V(b)=V(b’), where b’ is the best final board state that can be achieved starting from b and playing optimally until the end of the game.
  • Gives correct values but is not operational
state space search

m3 : bb3

m2 : bb2

m1 : bb1

State Space Search

V(b)= ?

V(b)= maxi V(bi)

state space search1
State Space Search

V(b1)= ?

V(b1)= mini V(bi)

m6 : bb6

m5 : bb5

m4 : bb4

final board states
Final Board States

Black wins: V(b)=-100

Blue wins: V(b)=100

draw: V(b)=0

number of board states

4 x 4 checkers:

Number of Board States

#board states < 8!*22/(2!*2!*4!) = 1680

Regular checkers (8x8 board, 8 pieces each)

#board states < 32!*216/(8! * 8! * 16!)= 5.07*1017

choose representation of target function
Choose Representation of Target Function
  • Table look-up
  • Collection of rules
  • Neural networks
  • Trade-off in choosing an expressive representation:
    • Approximation accuracy
    • Number of training examples to learn the target function
obtaining training examples
Obtaining Training Examples
  • V(b) : true target function
  • V’(b) : learned target function
  • Vtrain(b) : training value
  • Rule for estimating training values:
  • Vtrain(b)  V’(Successor(b))
main steps in designing a learning algorithm
Main steps in designing a learning algorithm
  • Determine training experience:
    • Historical data
    • Games against itself / experts
  • Determine target function
    • Board move / value
  • Determine representation of learned function
    • Linear combination of features
      • Determine learning algorithm
    • ANN
  • Finish design
  • As with any software product:
    • Implement
    • Test
    • Evaluate performance
why seminar
Why seminar?
  • A lot of open discussions
  • Basics in ML, and also state-of-the-art in applying ML to real world applications
  • Focus (and heavy grade weight) on active participation!
general structure for a seminar
General structure for a seminar
  • Topic announced ahead of time
    • Check regularly the seminar page
    • http://www.cs.unt.edu/~rada/CSCE5330
  • Presentation of basic material (Rada)
  • One, two, or more papers presented by you (a different speaker will be assigned each time), and discussed by the entire class
  • “Reviews” for each of the papers discussed are due before the seminar.
paper discussions
Paper discussions
  • Everybody has to read the papers before the discussion, and fill in a review form
    • Will be available from seminar webpage
  • Discussion leader:
    • Read the paper carefully; read an addition of 1-2 references
    • Prepare a presentation including:
      • Research goal
      • Methods applied
      • Research results
      • Comparison with other methods
      • Deficiencies of the paper
      • Directions for future research
    • Formulate questions and issues for class discussions
paper discussions cont d
Paper discussions (cont’d)
  • Discussion “scribe”
    • Take notes about the discussions made in class
    • Write a 2-3 page report on that, points made during the presentation, issues raised, questions, etc.
    • After seminar, discuss with the “discussion leader” to agree upon the content of the report
    • Submit report to the instructor within one day
conference in ml
Conference in ML
  • The seminar will “culminate” with a class conference
  • Term project
    • Topics to be discussed by next time, and decided in two weeks from now
    • Paper to be reviewed by your fellow colleagues
    • Conference (with presentations, discussions, etc) to be held during the last class meeting
administrivia
Administrivia
  • Course requirements:
    • Active participation
    • Homework assignments
      • 3 days late policy
    • Term project
  • Seminar webpage
    • http://www.cs.unt.edu/~rada/CSCE5330
administrivia1
Administrivia
  • Textbook
    • Machine Learning, by T. Mitchell
    • Recommended: Data Mining with Java (Weka), Witten & Frank
  • Software, data:
    • Weka package
      • Installed on CSP machines
    • Various data sets
grades
Grades
  • Assignments: 40%
  • Project: 40%
  • Class participation: 20%
course overview
Course Overview
  • Introduction to machine learning
  • Concept learning
  • Decision tree learning
  • Rule based learning
  • Evaluating hypotheses
  • Instance based learning
  • Neural networks
  • Bayesian learning
  • Hidden Marckov Models
  • Reinforcement learning
  • Boosting, bagging
  • Co-training, Self-training, Counter-training
possible course projects
Possible Course Projects
  • Apply machine learning techniques to your own problem e.g. classification, clustering, data modeling, object recognition
  • Investigate an ML research problem
  • Other
  • For next week:
    • Think of possible topics for a project.
    • I will propose several topics

Need to decide upon topic in four-six weeks from today.

next time
Next time
  • Concept learning
  • More on how to make a good presentation
    • Getting ready for the paper discussions
  • More on how to make a paper review
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