Readings
Readings for Fall, 2002
All members of the class (including auditors) are to read all of the assigned papers and meet in advance of the class in your reading groups to discuss them. The goal is that each group member should contribute her or his own insights and background knowledge to the small group discussions and hopefully clear up many confusions before we get to class. Topics that I would like you to cover in this discussion include some subset of:
- Does everybody understand the content of the paper? If not, what issues need to be clarified to improve understanding? Does the group have the collective knowledge to answer these questions, or does it require outside input? (E.g., from me, your other classmates, etc.) Feel free to come see me in office hours or to send mail to ml-class.
- What machine learning techniques are under investigation? Are these innovative ML algorithms, variants on existing algorithms, or well understood algorithms applied in novel ways or tested on novel data?
- What problem domain is under investigation? Did it benefit from the application of ML techniques? Did this approach solve the domain completely, or is further work necessary?
- How would you extend/improve this work? Did the authors make mistakes or oversimplifications? Is the proposal an approximation that could be improved? Could the ML algorithm be extended or a more sophisticated algorithm used in its place? Is there another (better, more interesting, or just novel) problem domain that this approach would apply to? (If so, what modifications to the approach would be necessary to apply it?)
Deliverables
Each group should turn in (at the beginning of class) a short (1-2 pages), typewritten summary of their discussion. Specifically, your writeup should include:- A summary of the content of the paper (1-2 paragraphs). Don't simply copy the abstract -- formulate your own summary of the paper. This should be both a description of what domain was studied, what ML algorithms were used, and the results of the study. (Hint: this is good practice at writing your own abstracts, for those who haven't done this much yet.)
- A description of how you would extend/improve this work (1-3 paragraphs). Again, please don't just take the authors' "future work" -- formulate your own thoughts about where to take this work. See the discussion points above for some starting places on this.
Finally, I want to encourage you to have fun with these papers. They seem pretty dry, but they're discussing some fascinating things and you'll really learn far more about ML in practice through these than through the high-level presentations that you get in lecture or the book.
Enjoy!
The Papers
- Sep 5, 2002
- Helman, P., Veroff, R., Atlas, S., and Willman, C. A Bayesian Network Classification Methodology for Gene Expression Data. Technical Report TR-CS-2002-18, Computer Science Department, University of New Mexico, 2002.
- Sep 19, 2002
-
- M. Mast, E. Nöth, H. Niemann, and E.G. Schukat-Talamazzini. Automatic Classification of Speech Acts with Semantic Classification Trees and Polygrams. In International Joint Conference on Artificial Intelligence 95, Workshop "New Approaches to Learning for Natural Language Processing", pages 71-78, Montreal, 1995.
- Young, S. J., Odell, J. J., and Woodland, P. C. Tree-Based State Tying for High Accuracy Acoustic Modelling [Follow the "Cached PS" or "Cached PDF" link to retrieve this paper]. In Proceedings ARPA Workshop on Human Language Technology, pages 307--312, 1994.
- Oct 8, 2002
-
- Fayyad, U. M., Weir, N, and Djorgovski, S. SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys. In International Conference on Machine Learning, (ICML-1993) (pp. 112-119). Not available online. Go to the library or see Terran for a copy.
- Chown, E., Dietterich, T. G. (2000). A Divide-and-Conquer Approach to Learning From Prior Knowledge. In International Conference on Machine Learning, (ICML-2000) (pp. 143-150).
- Oct 31, 2002
-
- Kamal Nigam, Andrew McCallum, Sebastian Thrun and Tom Mitchell Learning to Classify Text from Labeled and Unlabeled Documents. In AAAI-98.
- Andrew McCallum, Multi-Label Text Classification with a Mixture Model Trained by EM. Revised version of paper appearing in AAAI'99 Workshop on Text Learning.
- Nov 21, 2002
- The primary paper for this session is:
- Schaal, S., & Atkeson, C. G. "Robot juggling: An implementation of memory-based learning." Control Systems Magazine,14(1), 1994, pp.57-71.
