Learning Programs Danielle and Joseph Bennett and Lorelei

Learning Programs Danielle and Joseph Bennett (and Lorelei) 4 December 2007

Overview • • Definitions Types of Learning Why and Why Not? Programming Points Learning Machine Learning Common Applications Questions?

Machine Learning, Defined • “… the design and development of algorithms and techniques that allow computers to ‘learn’ ” • “… these programs develop concepts, infer new concepts from existing concepts and revise incorrect concepts”

Key Terms in Machine Learning • • • Knowledge base Noisy inputs Sparse but accurate Symbolic Explanation Based Learning systems (EBL)

Types of Learning • • • Supervised Unsupervised Semi-supervised Reinforcement Transduction Learning to learn

Why Learning Programs?

Why Learning Programs? • Differences in learning styles • Saves human time • Helps make advances in research

Why Not Learning Programs? • • • Difficult to program May require advance knowledge Supervision required in some cases Not suited for all tasks Human interaction cannot be eliminated

Points to Ponder • • Algorithm efficiency Programming language Avoid expensive operations (pointers) Limit parsing Don’t copy information Minimize code Limit library functions

In a Machine Learning Class… Bayesian theory • • • • • Modeling conditional probability density functions: regression and classification Artificial neural networks Decision trees Gene expression programming Genetic algorithms Genetic programming Inductive Logic Programming Gaussian process regression Linear discriminant analysis K-nearest neighbor Minimum message length Perceptron Quadratic classifier Radial basis function networks Support vector machines Algorithms for estimating model parameters Dynamic programming Expectation-maximization algorithm Modeling probability density functions through generative models • Graphical models including Bayesian networks and Markov Random Fields Generative Topographic Mapping • Approximate inference techniques • • • Monte Carlo methods Variational Bayes Variable-order Markov models Variable-order Bayesian networks Loopy belief propagation • • Optimization Meta-learning (ensemble methods) • • Boosting Bootstrap aggregating Random forest Weighted majority algorithm • • Inductive transfer and learning to learn Inductive transfer Reinforcement learning Temporal difference learning

Common Applications • • Natural language processing Syntactic pattern recognition Search engines Medical diagnosis Bioinformatics Cheminformatics Classifying DNA sequences

More Common Applications • • Detecting credit card fraud Stock market analysis (legal) Speech recognition Handwriting recognition Object recognition (in computer vision) Game playing Robot locomotion Intelligent tutoring

References • http: //en. wikipedia. org/wiki/Machine_learning • http: //www. lisa. org/globalizationinsider/2004/09/teaching_comput. html • http: //teach-computers. org/ • • • http: //hunch. net/? p=290 Learning programs, by Daniel St. Clair http: //www. machinelearning. net/

Conclusion • • • Definitions Types of Learning Why and Why Not? Programming Points Learning Machine Learning Common Applications

Questions?