1. Spring 2003 Dr. Susan Bridges
CS 9633 Machine Learning
Spring 2003
Dr. Susan Bridges
Adapted from notes by Tom Mitchell
Computer Science Department
CS 9633 KDD

2. Computer Science Department
Outline
What is machine learning?
What is a well-defined learning problem?
An example: learning to play checkers
What questions should we ask about machine learning?
Computer Science Department
CS 9633 KDD

3. Computer Science Department
Why Machine Learning?
Recent progress in algorithms and theory
Growing flood of data from many sources
World-wide web
Transaction data
Remotely sensed data
Genomic and proteomic data
Computational power now available
Budding industry
Computer Science Department
CS 9633 KDD

4. Machine Learning Research at MSU
Reinforcement Learning
Neural Networks
Genetic Algorithms
Data mining algorithms (fuzzy data mining)
Artificial immune systems
Clustering
And others
Computer Science Department
CS 9633 KDD

5. Application areas studied at MSU
Computer Security
Intrusion detection
Forensics
Bioinformatics
Genomics
Proteomics
Web indexing
Computer Science Department
CS 9633 KDD

6. Computer Science Department
Where is this headed?
Today
First generation algorithms
Neural networks
Decisions trees
Regression, …
Applied to well-formatted databases
Opportunities for Tomorrow
Learn across full mixed-media data
Learn across multiple internal databases, web, newsfeeds,etc.
Learn by active experimentation
Learn decisions rather than predictions
Cumulative, life-long learning
Programming languages with learning embedded
Computer Science Department
CS 9633 KDD

7. Computer Science Department
Relevant Disciplines
Artificial intelligence
Bayesian methods
Computational complexity theory
Control theory
Information theory
Philosophy
Psychology and neurobiology
Statistics …
Computer Science Department
CS 9633 KDD

8. What is a learning problem?
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 (legal moves)
P: % of games won in a world tournament
E: opportunity to play against self
Computer Science Department
CS 9633 KDD

9. Computer Science Department
Other examples
Learning to plan a party
Learning to detect intruders in a computer system
Learning to identify genes in a DNA sequence
Computer Science Department
CS 9633 KDD

10. Other Choices To Be Made
What type of experience?
What exactly should be learned?
How shall it be represented?
What specific algorithm to learn it?
Computer Science Department
CS 9633 KDD

11. Type of Training Experience
Direct or indirect?
Teacher or not?
A problem: is training experience representative of performance goal?
Computer Science Department
CS 9633 KDD

12. Choose the Target Function
ChooseMove: Board  Move ??
V: Board  R ?? …
Computer Science Department
CS 9633 KDD

13. Possible Definition of Target Definition V
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 state in the game, then V(b) = V(b’), where b’ is the best final board that that can be achieved starting from b and playing optimally until the end of the game.
This gives correct values, but is not operational
Computer Science Department
CS 9633 KDD

14. Choose Representation for Target Function
Collection of rules?
Neural network?
Polynomial function of board features? …
Computer Science Department
CS 9633 KDD

15. A Representation for Learned Function
bp(b): number of black pieces on board b
rp(b): number of red pieces on b
bk(b): number of black kings on b
rk(b): number of red kings on b
bt(b): number of red pieces threatened by black (i.e., which can be taken on black’s next turn)
rt(b): number of back pieces threatened by red
Computer Science Department
CS 9633 KDD

16. Obtaining Training Examples
Example of a training instance
One rule for estimating training values:
Computer Science Department
CS 9633 KDD

17. Choose Weight Tuning Rule
LMS Weight update rule:
Do repeatedly:
Select a training example b at random
1. Computer error(b)
2. For each board feature fi, update weight wi:
 is some small constant, say 0.1, to moderate the rate of learning
Computer Science Department
CS 9633 KDD

18. Computer Science Department
Final Decision
Experiment Generator
New problem
(initial game board)
Hypothesis
(V-hat)
Performance System
Generalizer
Critic
Training Examples
<bi, Vtrain(bi)>
Solution trace
(game history)
Computer Science Department
CS 9633 KDD

19. Some Issues in Machine Learning
What algorithms can approximate functions well (and when)?
How does the number of training examples influence accuracy?
How does complexity of hypothesis representation impact it?
How does noisy data influence accuracy?
What are the theoretical limits of learnability?
How can prior knowledge of the learner help?
What clues can we get from biological learning systems?
How can systems alter their own representations?
Computer Science Department
CS 9633 KDD