1. Machine learningOverview PD. Dr. Gabriella Kkai [email_address] Friedrich-Alexander-Universitt Lehrstuhl fr Informatik 2 Raum 04.131 Tel: 8528996

2. Machine Learning: Content

Why Machine Learning?

How can a learning problem be defined

Designing a learning system: learning to play checker

Perspectives and questions in ML

Summary

3. Why Machine Learning? (1/10)

Webster 's definition of 'learn'

• 'To gain knowledge, or understanding of, or skill in by study instruction or experience

Simons' definition (Machine Learning I, 1993, Chapter 2.)

• 'Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task or tasksdrawn from the same population more effectively the next time

Donald Michie's Definition (Computer Journal 1991)

• 'A learning system uses sample data to generate an update basis for improved (performance) on subsequent data from the same source and express the new basis in intelligible symbolic form'

4. Why Machine Learning? (2/10)

Machine learning istypically thought of as a sup-topic of artificial intelligence.

It is inspired by several disciplines

Machine Learning Cognitive Science Statistic PatternRecognition Computer Science 5. Why Machine Learning? (3/10)

Relevant topics:

• ArtificialIntelligence:Learning: Learning symbolic representation of concepts, ML as search problem , Prior knowledge + training examples guide the learning-process

• BayesianMethods:Calculatingprobabilitiesof the hypotheses, Bayesian-classifier

• Theory ofthe computational complexity:Theoretical boundsof the complexity for different learning task measured in the terms ofthe computational effort,number of differenttraining examples, the number ofmistakesrequired in order to learn

• Informationtheory:Measurement of the entropy, minimal description length, optimal codes and their relationship to optimal training sequences for encoding a hypothesis

• Philosophy:Occam's razor suggesting the simpliest hypothesis is the best

• Psychology and Neurobiology:Motivation of NN the power law of the practice

• Statistics : Characterisation of the errors (e.g. bias,variance),thatoccur when estimating the accuracy of hypothesis based, confidence interval, statistical tests

Goal: Description of the different learning paradigms, the algorithms, the theoretical results and applications

6. Why Machine Learning?(4/10)

Dimension: Constraints

• Task/objective

• • Learning task

• • Performance task

• Availability of the background knowledge

• • Encoded

• • Interactive

• Availability of data

• • Incremental vs. batch

• • Passive vs.active

• Characteristics of the data

• • Static vs. drifting

• • Propositional or first-order

7. Why Machine Learning?(5/10)

Dimension: Approach

• Search mechanism

• • Top-Down (model driven)

• • Bottom-up (data driven)

• • Many others

• Reasoning methods

• • Induction, abduction, deduction

8. Why Machine Learning? (6/10)

Deductive Reasoning:

Inductive Reasoning:

Abductive Reasoning:

9. Why Machine Learning? (7/10)

• Evaluation Methodologies

• • Mathematical

• • • Previously: Learning in the limit

• • • Now: PAC (Probably Approximately Correct)

• • • • More tolerant

• • • • Addresses efficiency constraints

• • • Recent:

• • • • Best cases analysis (Helpful Teacher Model)

• • • • Average case analysis (constraining assumption)

• • Empirical:When mathematical analysis isn't obvious

• • • Popular

• • • Data intensive

• • Psychological

• • • Goal: Model human learning behaviour

• • • Method: Comparison with subject data

10. Why Machine Learning? (8/10)

Knowledge-Poor Supervised Learning

• Given:A training set of annotated instances

• To Induce : A hypothesis (concept description)

Knowledge-Intensive Supervised Learning

• Given :A set of training instances + a hypothesis of the target concept +background knowledge

• To Induce:A modified hypothesis (concept description)that is consistent with the domain theory & the training instances

Unsupervised learning: clustering

• Given: A set of unclassified instancesI Have not any special target attribute

• To Do:Create a set of clusters forIaccordingto their presumed classes Clusters need not to be disjoint Clusters can be hierarchically related

11. Why Machine Learning? (9/10)

Paradigms knowledge-poor supervised learning:

• Concept learning

• Decision tree (ID3, TIDT)

• Rule based

• Lazy learning

• Genetic algorithms

• Neural networks

• Bayesian networks

Paradigms knowledge-intensive supervised learning:

• Explanation based learning

• Inductive Logic Programming

Unsupervised learning

• Bayesian learning

• Clustering

12. Why Machine Learning? (10/10)

Importance: How can computers be programmed that they 'learn'

Machine learningnatural learning

Application areas

• Data mining: automatic detection of regularity in big amounts of data

• Implementation of software, which cannot be easily programmed by hand

• Self adaptive programs: programs for playing

Theoretical results:Connection among the number of training examples, the hypothesis and the expected error

Biological studies

13. How can the learning problem be defined

Definition:A computer program is said tolearnfrom experienceEwith respect to some class of tasksTand performance measureP , if its performance at tasks inT , as measured byPimproves with experienceE

Example: Learning to play checker

• TaskT : design a program to learn to play checker

• Performance measureP : The percentage of the games won

• ExperienceE : Playing against itself

14. Content

Why Machine Learning?

How can the learning problem be defined

• Choosing the training experience

• Choosing the target function

• Choosing the representation of the target function

• Choosing a function approximation algorithm

Designing a learning system: learning to play checker

Perspectives and questions in ML

Summary

15. Choosing the Training Experience (1/2)

What experience is provided

• Direct or indirect feedback regarding the choices executed by the system

• • Direct:Individual checker board states and the correct move for each

• • Indirect:move sequences and final outcomesProblem:determiningthe degree to which each move in the sequence deserves credit or blame for the final outcome ( credit assignment )

• The rate of the controls of the sequence of the training examples by the learning system

• • Theteacher selectsinformative board states and provides the correct move for each

• • Thelearnermight itselfpropose board statesthat it finds particularly confusing and ask the teacher for the correct move

• • The learner may have complete control over both the board states and the (indirect) training classification, as it does when it learns playing against itself with no teacher

16. Choosing the Training Experience (2/2)

• How well does it represent the distribution of examples over which the final system performancePmust be measured Problem:The distribution of the training examplesis identical to the distribution of the test examples

• A checkers learning problem:

• • Task T : playing checker

• • Performance measure P : percentage of games won in the world tournament

• • Training experience E : games played against itself

17. Choosing the Target Function (1/2)

What type of knowledge will be learned and howwill thisbe used by the performaning program

• Example:The program needs to learn how to choose the best move from any board state

• ChooseMove: B:the set of legal board stateM:the set of legal moves

• Problem:difficult to learn if only the kind of indirect training experience is available to our system=> B : the set of legal board states: some real value

18. Choosing the Target Function (2/2)

Question:Definition of the target functionV :

• Ifbisa final board state that is won, then

• Ifbis a final board state that is lost, then

• Ifb is a final board state that is drawn, then

• Ifbis not a final state in the game, thenwhereb ' is the best final board state that can be achievedstarting fromband playing optimally until the end of the game (assuming the opponent plays optimally as well).

Problem:While this definition specifies a value ofV(b)for every board stateb recursively , this definition is not usable by our checker's player because it isnot efficiently computable

Solution:Discovering anoperational descriptionof the ideal target functionV ,Difficult=>learning some approximation

19. Choosing a Function ApproximationAlgorithm (1/2)

How canbe represented?

• For any given board state, the functionwill be calculated as a linear combination of weights

• • bp(p):the number of black pieces on the board

• • rp(b):the number of red pieces on the board

• • bk(b): the number of black kings on the board

• • rk(b):the number of red kings on the board

• • bt(b):the number of black pieces threatened by red (i.e., which can be captured on red's next turn)

• • rt(b): the number of red pieces threatened by black

20. Choosing a Function ApproximationAlgorithm (2/2)

Partial design of a checker learning program:

• Task T:playing checker

• Performance measure P:percentage of games won in the world tournament

• Training experience E:games played against itself

• Target function

• Target function representation :

21. Choosing a Function Approximation Algorithm: Estimating Training Values

How to assign training values to the more numerousintermediateboard states?

• Approach:assign the training value offor any intermediate board statebto be,whereis the learner's current approximation toVandwhereSuccessor(b)denotes the next board state followingb for which it is again the program's turn to move.

• Rule for estimating the training values:

22. Choosing a Function Approximation Algorithm: Adjusting the Weights

LMS Weight update rule(choosing the weightsto best fit the set of training examples)

• Best fit:minimise the squared error E between the training values and the valuespredicted by the hypothesis:

• For each training example

• • Use the current weights to calculate:

• • For eachupdate cis a small constantthat moderates the size weight update.

23. Some Issues in Machine Learning

What algorithms can approximate functions well (and when?)

How doesthe number of training examples influence the accuracy?

How does the complexity of the hypothesis representation impact it?

How does noisy data influence the accuracy?

What are the theoretical limits of learnability?

How can prior knowledge of the learner help?

What clues can we get from abiological learning system?

How can systems alter their own representation?

24. Summary

Goal:Building computer programs that improve their performanceat some task through experience

Application domain:

• Data Mining:discover automaticallyimplicit regularities in large data sets

• Poorly understood domains where humans might not have the knowledge needed to develop effective algorithms

• Domains where the program must dynamically adapt to changing conditions

ML draws on ideas from several sets of disciplines, including artificial intelligence, probability and statistics, computational complexity information theory, psychology and neurobiology, control theory and philosophy

Well defined learning problem =well specified task+ performance metric + source of training examples