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CEN 559 Machine Learning 2011-2012 Fall Term

DEPARTMENT of COMPUTER SCIENCE and INFORMATION TECHNOLOGIES. CEN 559 Machine Learning 2011-2012 Fall Term. Dr . Abdülhamit Subaşı asubasi @ ibu.edu.ba. Office Hour: Open Door Policy Class Schedule: Monday 1 7 :00-1 9 : 45. Course Objectives.

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CEN 559 Machine Learning 2011-2012 Fall Term

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  1. DEPARTMENT of COMPUTER SCIENCE and INFORMATION TECHNOLOGIES CEN 559 Machine Learning 2011-2012 FallTerm Dr. Abdülhamit Subaşıasubasi@ibu.edu.ba

  2. Office Hour: Open Door Policy • Class Schedule:Monday 17:00-19:45

  3. Course Objectives • Present the key algorithms and theory that form the core of machine learning. • Draw on concepts and results from many fields, including statistics, artifical intelligence, philosophy, information theory, biology, cognitive science, computational complexity, and control theory.

  4. Textbooks Du and Swamy, Neural Networks in a Softcomputing Framework, Springer-Verlag London Limited, 2006. Sebe, Cohen, Garg and Huang, Machine Learning in ComputerVision, Springer, 2005. Chow and Cho, Neural Networks and Computing, Imperial College Press, 2007. Mitchell T., Machine Learning, McGraw Hill, 1997. T. Hastie,R. Tibshirani, J. Friedman,The Elements of Statistical Learning, Second Edition, Springer, 2008.

  5. Brief Contents • Introduction • Concept Learning • Decision Tree Learning • Artificial Neural Networks • Evaluation Hypotheses • Bayesian Learning • Computational Learning Theory • Reinforcement Learning

  6. Grading Midterm Examination 25% Research & Presentation 25% Final Examination 50% Minimum 15 pages word document, related PPT and presentation

  7. Research Topics: • Linear Methods for Classification • Linear Regression • Logistic Regression • Linear Discriminat Analysis • Perceptron •  Kernel Smoothing Methods Ref5 • Kernel Density Estimation and Classification (Naive Bayes) • Mixture Models for Density Estimation and Classification • Radial Basis Function Networks - Ref1 • Basis Function Networks for Classification – Ref3 • Advanced Radial Basis Function Networks– Ref3 • Fundamentals of Machine Learning and Softcomputing –Ref1 • Neural Networks Ref5 • Multilayer Perceptrons- Ref1 • Hopfield Networks and Boltzmann Machines - Ref1 • SVM Ref5 • KNN Ref5 • Competitive Learning and Clustering - Ref1 • Unsupervised Learning k means Ref5 • Self-organizing Maps– Ref3

  8. Research Topics: • PrincipalComponentAnalysis Networks (PCA, ICA)- Ref1 • FuzzyLogicandNeurofuzzySystems - Ref1 • EvolutionaryAlgorithmsandEvolvingNeural Networks (PSO) - Ref1 • Discussionand Outlook (SVM, CNN, WNN) - Ref1 • DecisionTreeLearningDuda&Hart • RandomForest Ref5 • PROBABILISTIC CLASSIFIERS-REF2 • SEMI-SUPERVISED LEARNING-REF2 • MAXIMUM LIKELIHOOD MINIMUM ENTROPY HMM-REF2 • MARGIN DISTRIBUTION OPTIMIZATION-REF2 • LEARNING THE STRUCTURE OF BAYESIAN NETWORK CLASSIFIERS-REF2 • OFFICE ACTIVITY RECOGNITION-REF2 • Model AssessmentandSelection REF5 • Cross-Validation • BootstrapMethods • Performance ROC, statistic • WEKA MachineLearningTool • TANGARA MachineLearningTool • ORANGE MachineLearningTool • NETICA MachineLearningTool • RAPID MINER MachineLearningTool

  9. What is Machine Learning? Machine learning is the process in which a machine changes its structure, program, or data in response to external information in such a way that its expected future performance improves. Learning by machines can overlap with simpler processes, such as the addition of records to a database, but other cases are clear examples of what is called “learning,” such as a speech recognition program improving after hearing samples of a person’s speech.

  10. Components of a Learning Agent • Curiosity Element – problem generator; knows what the agent wants to achieve, takes risks (makes problems) to learn from • Learning Element – changes the future actions (the performance element) in accordance with the results from the performance analyzer • Performance Element – choosing actions based on percepts • Performance Analyzer – judges the effectiveness of the action, passes info to the learning element

  11. Why is machine learning important? Or, why not just program a computer to know everything it needs to know already? Many programs or computer-controlled robots must be prepared to deal with things that the creator would not know about, such as game-playing programs, speech programs, electronic “learning” pets, and robotic explorers. Here, they would have access to a range of unpredictable knowledge and thus would benefit from being able to draw conclusions independently.

  12. Relevance to AI • Helps programs handle new situations based on the input and output from old ones • Programs designed to adapt to humans will learn how to better interact • Could potentially save bulky programming and attempts to make a program “foolproof” • Makes nearly all programs more dynamic and more powerful while improving the efficiency of programming.

  13. Approaches to Machine Learning • Boolean logic and resolution • Evolutionary machine learning – many algorithms / neural networks are generated to solve a problem, the best ones survive • Statistical learning • Unsupervised learning – algorithm that models outputs from the input, knows nothing about the expected results • Supervised learning – algorithm that models outputs from the input and expected output • Reinforcement learning – algorithm that models outputs from observations

  14. Current Machine Learning Research Almost all types of AI are developing machine learning, since it makes programs dynamic. Examples: • Facial recognition – machines learn through many trials what objects are and aren’t faces • Language processing – machines learn the rules of English through example; some AI chatterbots start with little linguistic knowledge but can be taught almost any language through extensive conversation with humans

  15. Future of Machine Learning • Gaming – opponents will be able to learn from the player’s strategies and adapt to combat them • Personalized gadgets – devices that adapt to their owner as he changes (gets older, gets different tastes, changes his modes) • Exploration – machines will be able to explore environments unsuitable for humans and quickly adapt to strange properties

  16. Problems in Machine Learning • Learning by Example: • Noise in example classification • Correct knowledge representation • Heuristic Learning • Incomplete knowledge base • Continuous situations in which there is no absolute answer • Case-based Reasoning • Human knowledge to computer representation

  17. Problems in Machine Learning • Grammar – meaning pairs • new rules must be relearned a number of times to gain “strength” • Conceptual Clustering • Definitions can be very complicated • Not much predictive power

  18. Successes in Research • Aspects of daily life using machine learning • Optical character recognition • Handwriting recognition • Speech recognition • Automated steering • Assess credit card risk • Filter news articles • Refine information retrieval • Data mining

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