CS 546
Advanced Topics in Machine Learning
Spring Quarter 2010
Time : Tuesdays and Thursdays, 2:00-3:50pm
Location: Campus Rec. Center (ASRC), Room 220.
Instructor: Melanie Mitchell,
FAB 120-24, (503) 725-2412, mm-AT-cs.pdx.edu.
Office hours: Tuesdays and Thursdays, 4:00-5:00pm, or by appointment.
Course Website: :
http://www.cs.pdx.edu/~mm/aml2010/index.html
Prerequisites: CS 445/545, or equivalent.
Projects: Each student will carry out a term-long research project in some area of machine learning. Each student will give a presentation and write a paper on their project by the end of the term. Each student will also be assigned to review/edit some number of other students' papers.
Paper presentations: Each student will present in class some number of recent papers related to their project
Reading: There will be no textbook. Required reading will handed out in class or accessible via the Web.
Exams: There will be no exams.
Grading: Students will be graded on their class presentations, class participation, and final project talk and paper.
Academic integrity: Students will be responsible for following the PSU Student Conduct Code, and in particular, the policy concerning academic honesty.
Students with disabilities: If you are a student with a disability in need of academic accommodations, you should register with Disability Services for Students and notify the instructor immediately to arrange for support services.
Class schedule (subject to change):
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Date |
Topics and Reading |
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Tuesday March 30 |
Course
intro Discussion of research areas S. Wooldridge, Bayesian Belief Networks |
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Thursday April 1 |
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Tuesday April 6 |
Students present research proposals and reading lists Serre et al. Robust object recognition with cortex-like mechanisms Optional reading: Russell and Norvig, Chapter 15, sections 1-3. L. I. Smith, A tutorial on Principal Components Analysis |
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Thursday April 8 |
Topic:
Understanding Classification Decisions of Additive
Classifiers Poulin et al, Visual Explanation of Evidence in Additive Classifiers. Conference on Innovative Applications of Artificial Intelligence, July 2006. Robnik-Sikonja and Kononenko, Explaining Classifications for Individual Instances. IEEE Trans. on Knowledge and Data Engineering, May 2008, vol. 20(5). |
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Tuesday April 13 |
Topic: Weighted and Fuzzy SVMs, Semi-supervised SVMs Presenter: Josh Hoak Reading:Lin and Wang, Training algorithms for fuzzy support vector machines with noisy data Wang and Chiang, Fuzzy support vector machine for multi-class text classification |
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Thursday April 15 |
Topic: Simultaneous localization and mapping for embedded agents Presenter: Spenser Barlow Reading:Montmerlo et al., FastSLAM: A factored solution to the simultaneous localization and mapping problem Dellaert et al. Monte Carlo localization for mobile robots Optional:Arulampalam et al., A Tutorial On Particle Filters for Online Non-Linear Non-Gaussian Bayesian Tracking |
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Tuesday April 20 |
Topic: Evolution and coevolution of cellular automata and random Boolean networks Presenter: Alireza Goudarzi Reading:Mitchell, M (2006). Coevolutionary learning with spatially distributed populations Pagie, L. and Mitchell, M. (2002). A comparison of evolutionary and coevolutionary search Carnevali, S. and Patarnello, P. 1989. Learning Networks of Neurons with Boolean Logic |
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Thursday April 22 |
Topic: The Baldwin effect: Interactions between evolution and learning Presenter: Jason Akers Reading: Genes, Phenes and the Baldwin Effect: Learning and Evolution in a Simulated Population . RM French and A Messinger. Artificial Life IV 1994.Lamarckian Evolution, The Baldwin Effect, and Function Optimization. D. Whitley, VS Gordon, K Mathias Optional: A Hybrid Self-Adaptive Genetic Algorithm Based on Sexual Reproduction and Baldwin Effect for Global Optimization. Zhang, Zhao, Wang. IEEE 2009. |
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Tuesday April 27 |
Topic: Genetic algorithms for feature selection Presenter: Jeff Weston Reading: Oh et al., Hybrid genetic algortihms for feature selectionDerrac et al., IFS-CoCo: Instance and Feature Selection Based on Cooperative Coevolution with Nearest Neighbor Rule |
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Thursday April 29 |
Topic: Extensions of the HMAX model Presenter: Jason Hall (MM away) Reading: J. Mutch and D. G. Lowe, Object class recoginition and localization using sparse features with limited receptive fields Wu et al., Object recognition by learning informative, biologically inspired visual features T. Masquelier and S. J. Thorpe, Unsupervised learning of visual features through spiketiming dependent plasticity |
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Tuesday May 4 |
Topic: MicroRNAs, cancer, and multi-class classification Presenter: David Gibbs Reading:Freund, An adaptive version of the boost by majority algorithm Lu et al., MicroRNA expression profiles classify human cancers |
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Thursday May 6 |
Topic: Classifying and designing peptides Presenter: Chris Whelan Reading:C. Leslie, E. Eskin, J. Weston, and W. Noble, Mismatch string kernels for SVM protein classification, Advances in Neural Information Processing Systems, pp. 1441–1448, 2003. L. Jacob, B. Hoffmann, V. Stoven, and J.-P. Vert, Virtual screening of GPCRs: an in silico chemogenomics approach, BMC Bioinformatics 2007, vol. 9, p. 363, Jan 2008. |
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Tuesday May 11 |
Topic: Model order and cue combination in visual recognition Presenter: Karan Sharma Reading:A. Rabinovich, A. Vedaldi, and S. Belongie, Does Image Segmentation Improve Object Categorization? A. Rabinovich, T. Lange, J. Buhmann, and S. Belongie, Model Order Selection and Cue Combination for Image Segmentation |
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Thursday May 13 |
Topic: Hierarchical Temporal Memory (HTM) Presenter: Ryan Price Reading:George and Jaros, The HTM learning algorithms George and Hawkins, Toward a mathematical theory of cortical micro-circuits (optional) |
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Tuesday May 18 |
Topic: TBA Presenters: Max Orhai and Bert Shaw Reading:For Bert's presentation EJ Pauwels and G Frederix, Finding salient regions in images. Nonparametric clustering for image segmentation and grouping HD Cheng and Y Sun, A hierarchical approach to color image segmentation using homogeneity For Max's presentation M. Tomassini, Generalized Automata Networks S. Harding et al., Self-Modifying Cartesian Genetic Programming |
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Thursday May 20 |
Topic: Feedback and attention in the HMAX model Presenter: Max Quinn Readings: F. Miau and C. Papageorgiou, Neuromorphic algorithms for computer vision and attention |
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Tuesday May 25 |
No class |
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Thursday May 27 |
First draft of project papers due; papers assigned to reviewers Topic: System identification with recurrent neural networks Presenter: Josh Hughes Readings: |
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Tuesday June 1 |
Topic: Motor control and memory Readings: |
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Thursday June 3 |
Reviews of project papers due Student project presentations |
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Monday June 7 |
10:15-12:05: Student project presentations |
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Wednesday June 9 (Make-up class) |
10:15-12:05: Student project presentations Note change of location to FAB 150. |
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Friday June 11 |
Final project papers due |