1. Machine learning Overview
PD. Dr. Gabriella Kókai
Friedrich-Alexander-Universität
Lehrstuhl für Informatik 2
Raum Tel:

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 tasks drawn 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 is typically thought of as a sup-topic of artificial intelligence.
It is inspired by several disciplines
Computer
Science
Cognitive
Science
Machine
Learning
Pattern
Recognition
Statistic

5. Why Machine Learning? (3/10)
Relevant topics:
Artificial Intelligence:Learning: Learning symbolic representation of concepts, ML as search problem , Prior knowledge + training examples guide the learning-process
Bayesian Methods:Calculating probabilities of the hypotheses, Bayesian-classifier
Theory of the computational complexity: Theoretical bounds of the complexity for different learning task measured in the terms of the computational effort, number of different training examples, the number of mistakes required in order to learn
Information theory: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), that occur 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 instances I Have not any special target attribute
To Do: Create a set of clusters for I according to 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 learning natural 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 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 E
Example: Learning to play checker
Task T: design a program to learn to play checker
Performance measure P: The percentage of the games won
Experience E: 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 outcomes Problem: determining the 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
The teacher selects informative board states and provides the correct move for each
The learner might itself propose board states that 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 performance P must be measured Problem: The distribution of the training examples is 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 how will this be 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 state M: 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 function V:
If b is a final board state that is won, then
If b is a final board state that is lost, then
If b is a final board state that is drawn, then
If b is not a final state in the game, then where b' is the best final board state that can be achieved starting from b and playing optimally until the end of the game (assuming the opponent plays optimally as well).
Problem: While this definition specifies a value of V(b) for every board state b recursively, this definition is not usable by our checker's player because it is not efficiently computable
Solution: Discovering an operational description of the ideal target function V, Difficult => learning some approximation

19. Choosing a Function Approximation Algorithm (1/2)
How can be represented?
For any given board state, the function will 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 Approximation Algorithm (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 numerous intermediate board states?
Approach: assign the training value of for any intermediate board state b to be , where is the learner's current approximation to V and where Successor(b) denotes the next board state following b 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 weights to best fit the set of training examples)
Best fit:minimise the squared error E between the training values and the values predicted by the hypothesis:
For each training example
Use the current weights to calculate:
For each update c is a small constant that moderates the size weight update.

23. Some Issues in Machine Learning
What algorithms can approximate functions well (and when?)
How does the 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 a biological learning system?
How can systems alter their own representation?

24. Summary
Goal: Building computer programs that improve their performance at some task through experience
Application domain:
Data Mining: discover automatically implicit 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