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Outline of the Topics Covered in the Machine Learning Interface Course : (see full outline for more detail)

Outline of the Topics Covered in the Machine Learning Interface Course : (see full outline for more detail). Marc Sobel. Stat 9180: Topics for the interface between Statistics, Statistical Learning, Machine Learning, Data Mining, and Computer Vision

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Outline of the Topics Covered in the Machine Learning Interface Course : (see full outline for more detail)

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  1. Outline of the Topics Covered in the Machine Learning Interface Course : (see full outline for more detail) Marc Sobel

  2. Stat 9180: Topics for the interface between Statistics, Statistical Learning, • Machine Learning, Data Mining, and Computer Vision • Time: Monday Evenings: 7:15-9:25, Fall Semester 2007. • Place: Tuttleman 401B. • Course Number: Old=701; New=9180. • Instructor: Marc Sobel, Department of Statistics, Temple University. • Office: 338 Speakman Hall.

  3. Introduction • This course is designed to cover Bayesian and statistical learning topics relevant to the fields of Machine learning, Data Mining, and Computer Vision. Prerequisites for the course include a knowledge of lower level algebra and pre-calculus. Students must complete a semester project dealing with one or more of the area’s listed below for credit. Projects can be concerned with the statistical techniques themselves or with relevant applications. I will suggest possible projects throughout the course. The course will cover statistical techniques with applications including the following:

  4. Topics Discussed • 1. Clustering: the interface between k-means, EM based clustering, enhanced k-means clustering. • 2. Bayes Theorem: Occam’s Razor and the reason for avoiding classical statistics. The advantages of Bayes theorem. • 3. Markov Chain Monte Carlo in Computational Analysis. • 4. Boosting in statistics and machine learning

  5. Topics (continued) • 5. The role of ‘distance’ and ‘density’ in formulating statistical models. The special role of Kullback Leibler Divergence. • 6. Sequential Markov Chain Monte Carlo: Using Bayesian filters, particles to solve problems in inference. • 7. Robot Mapping and the alignment of maps

  6. Topics (More) • 8. Statistics and Shape Theory • 9. The use of robust statistical techniques for clustering and inference. • 10. Random Fields and Hidden Markov Models in applications. • 11. Additional Topics?

  7. Bibliography: (The titles in red are of particular interest/value for the course) • Bibliography • [1] Anderson, Ted, An introduction to Multivariate Statistical Analysis, Wiley-Interscience, 2003. • [2] Baldi, P., and Brunak, S. Bioinformatics: the machine learning approach, MIT Press. • [3] Carlin, B.P., and Louis, T.A., Bayes and empirical bayes methods for data analysis, Chapman and Hall, 1996. • [4] Cox, Trevor F., Multidimensional Scaling, Chapman and Hall, 2001. • [5] Doucet, A., Freitas N., Gordon, N. Sequential Monte Carlo Methods in Practice, Springer, 2001 • [6] Eaton, Morris, Multivariate Statistics: a vector space approach, Wiley, 1983. • [7] Frey, B. Graphical Models for Machine Learning and Digital Communication, MIT Press, 1998. • [8] Hardle, Wolfgang, Smoothing Techniques, Springer, 1990. • [9] Hardle, Wolfgang, Nonparametric and semiparametric models, Springer 2004.

  8. Bib (more) • [10] Hsu, Jason, Multiple Comparisons: Theory and Methods, Chapman and Hall, 1996. • [11] Huber, Peter Robust Statistical Procedures, SIAM, 1996. • [12a] Krim, H. and Yezzi A. Statistics and Shape Analysis • [12] Li, Stan Z. Markov Random Field Modeling in Image Analysis, Springer Computer Science Workbench, 2001. • [13] Liu, Jun S., Monte Carlo Strategies in Scientific Computing, Springer, 2001 • [14] Mackay, David Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003. • [15] Neal, Radford, Bayesian Learning for Neural Networks, Springer, 1996. • [16] Rousseeuw, Peter W. Robust regression and outlier detection, Wiley-Interscience, 2003. • [17] Schmidli, Heinz, Reduced rank regression: with applications to quantitative structure-activity relationships, Physica-Verlag, 1995.

  9. Bib (more) • [18] Tanner, Martin, Tools for Statistical Inference; Methods for the exploration of Posterior Distributions and Likelihood Functions, Springer, 1996 • [19] Thrun, Sebastian, Burgard, and Fox, Probabilistic Robotics, • [20] Hastie, Tibshirani, and Friedman, The elements of Statistical Learning, Springer 2001. • [21] Timm, Neil H. Applied multivariate analysis, Springer 2006. • [22] Tapia, R., and Thompson, J.R., Nonparametric Density Estimation, Johns Hopkins, 1978. • [23] Vapnik, Vladimir, The nature of Statistical Learning, Springer, Second Edition, 2000. • [24] Weisberg, Sanford, Applied Linear Regression, Wiley, 1995. • [25] Wilcox, Rand R., Introduction to robust estimation and hypothesis testing, Academic Press, 1997. • [26] Winkler, Gerhard, Image Analysis, Random Fields, and Dynamic Monte Carlo Methods, A Mathematical Introduction, Springer 2003

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