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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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