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
• 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.
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:
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
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
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?
Bibliography: (The titles in red are of
particular interest/value for the course)
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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.
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.
Bib (more)
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[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