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### Seminar on Machine LearningRada Mihalcea

Introduction to Machine Learning

Administrivia

January 13(!), 2004

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

- Artificial intelligence
- Control theory
- Information theory
- Computational complexity theory
- Psychology and neurobiology
- Statistics

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

- 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: 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

- 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

- 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

- ChooseMove : B M : board state move
- Maps a legal board state to a legal move
- Evaluate : BV : 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

- 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

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

- 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

- 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

- 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?

- 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

- 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

- 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)

- 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

- 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

- Course requirements:
- Active participation
- Homework assignments
- 3 days late policy
- Term project
- Seminar webpage
- http://www.cs.unt.edu/~rada/CSCE5330

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

- Assignments: 40%
- Project: 40%
- Class participation: 20%

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

- 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

- 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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