Spring 2003 Dr. Susan Bridges CS 9633 Machine Learning Spring 2003 Dr. Susan Bridges Adapted from notes by Tom Mitchell http://www-2.cs.cmu.edu/~tom/mlbook-chapter-slides.html Computer Science Department CS 9633 KDD

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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