Graduate
Statistics 9180 (Fall, 2007) Outline: Usefull texts: MHpaper ,Machine Learning Paper, Neal Book, MacKay Book. Boosting papers: BasicBoosting, Freund-Shapire article; stochastistic boosting; mars, bart. geometric mds paper; manifold mds paper; marginal slam I; marginal slam II; estimating map features, Hidden Markov Models with applications to speech recognition; the smyth paper concerning event detection the smyth kdd paper

Topics Covered:Lecture 1: Clustering, Lecture 1,8: Model Comparison,Bayesian Inference, Lecture 2: MCMC, Lecture 3: MCMC (part II) and MCMC-Particle Theory; Lecture 4: Field Theory for Image Analysis; Lecture 8: Decision Theory for Machine Learning, Lecture 5: Boosting Lecture 6: More boosting and SOM: Lecture 7: Multidimensional Scaling; Lecture 9: Statistical Theory of Shape ; Lecture 10: Marginal Partical Filters and their application to (multi) robot slam; Lecture 11: Hidden Markov Models with applications to speech recognition Lecture 12: Event Detection