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

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

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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”

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

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

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Why Learning Programs?

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Differences in learning styles Saves human time Helps make advances in research

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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

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

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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

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

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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

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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/

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

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Questions?

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