CS260: Seminar in Machine Learning

Fall 2004

Location: Gordon Watkins Hall 2141
Time: Tuesday and Thursdays 11:10am - 12:30pm
Instructor: Christian Shelton (Office Hours: Wednesdays 3:00pm - 5:00pm)

Topics (and suggested papers):

TopicSubtopicPapers
Supervised LearningBoosting Robert E. Schapire. The boosting approach to machine learning: An overview. In MSRI Workshop on Nonlinear Estimation and Classification, 2002.
Yoav Freund and Robert E. Schapire. Experiments with a new boosting algorithm. Machine Learning: Proceedings of the Thirteenth International Conference, pages 148-156, 1996.
Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5):1651-1686, 1998.
Support Vector Machines Christopher J.C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. 1998
Unsupervised Learning Principal Component Analysis Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller. Kernel Principal Component Analysis. In Advances in Kernel Methods: Support Vector Learning, edited by Bernhard Schölkopf, Christopher J.C. Burges, and Alexander J. Smola, 1999.
Independent Component Analysis A. Bell and T.J. Sejnowski. An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation, 7:1129-1159, 1995.
Erik Learned-Miller and John Fisher. ICA Using Spacings Estimates of Entropy. Journal of Machine Learning Research, 4:1271--1295, 2003.
Clustering Jon Kleinberg. An Impossibility Theorem for Clustering. NIPS 2002.
Andrew Y. Ng, Michael I. Jordan, Yair Weiss. On Spectral Clustering: Analysis and and algorithm. NIPS 2001.
Francis R. Bach and Michael I. Jordan. Learning Spectral Clustering. TR UCB/CSD-03-1249, UC Berkeley, Dept of CS, June 2003.
Eamonn Keogh, Stefano Lonardi, and Chotirat Ann RatanamahatanaTowards Parameter-Free Data Mining SIGKDD, 2004.
Semi-Supervised Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty, Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. ICML, 2003.
Reinforcement Learning MDPTBD
POMDP Michael Lederman Littman. Algorithms for Sequential Decision Making. Ph.D. disseration and TR CS-96-09, Brown University, Dept of CS, March 1996. (selected portions)
Zhengzhu Feng and Shlomo Zilberstein. Region-Based Incremental Pruning for POMDPs. UAI 2004.
Nicholas Roy and Geoffrey Gordon. Exponential Family PCA for Belief Compression in POMDPs. NIPS 2002.
Density Estimation/Inference Bayesian NetworksTDB
Dynamic Systems K. Murphy. Filtering, Smoothing, and the Junction Tree Algorithm. Tech Report 1998.
M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing. 50(2), 174-188. 2002.
Xavier Boyen and Daphne Koller. Exploiting the Architecture of Dynamic Systems. AAAI 1999.
Arnaud Doucet, Nando de Freitas, and Neil Gordon. An Introduction to Sequential Monte Carlo Methods. In Sequential Monte Carlo Methods in Practice, ed. by Arnaud Doucet, Nando de Freitas, and Neil Gordon. 2001.
Kevin Murphy and Stuart Russell. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. UAI 2000.
Michael Montemerlo, Sebastian Thrun, Daphne Koller, and Ben Wegbreit. FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem. AAAI 2002.
Mark A. Paskin. Thin Junction Tree Filters for Simultaneous Localization and Mapping. NIPS 2003.
Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas, and Eric Wan. The Unscented Particle Filter. TR CUED/F-INFENG/TR 380, Cambridge University, Dept. of Eng. May 2000.

Schedule

Week #TuesdayThursday
 DateTopicPaper(s)Leader           DateTopicPaper(s)Leader          
0Sept 23IntroN/AChristian
1 Sept 28Boosting (basics) Freund & Schapire and Schapire Titus Sept 30Boosting (bounds) Schapire et. al. and Schapire Kin
2 Oct 5SVMs Burges Guobiao Oct 7PCAN/AChristian
3 Oct 12Kernel PCA Schölkopf et al.Jing Oct 14ICA part I Bell and Sejnowski Xiaopeng
4 Oct 19ICA part II Learned-Miller and Fisher Christian Oct 21Spectral Clustering Ng, Jordan, and Weiss Ryan
5 Oct 26Impossibility of Clustering Kleinberg Matt Oct 28Compression and Clustering Keogh, Lonardi, and Ratanamahatana Titus
6 Nov 2Semi-Supervised Learning Zhu, Ghahramani, and Lafferty Jing Nov 4MDPs Littman (intro, ch 1, ch 2) Guobiao
7 Nov 9MDPs Littman Guobiao / Kin Nov 11---Holiday---
8 Nov 16POMDPs Littman (ch 6-8) Kin Nov 18POMDP planning Roy and Gordon Matt
9 Nov 23FilteringN/AChristian Nov 25---Holiday---
10 Nov 30Partical Filtering Arulampalam et al. Xiaopeng Dec 2SLAM Montemerlo et al. Ryan