CSI 5325 paper presentations

Here we will organize the papers that the students in the class will present. Typically, we will have a presentation on one paper relevant to the topic of discussion shortly after the professor has finished his lectures on the topic.

I will put up a list of potential papers for each topic; it's up to the presenter to choose the paper at least a week prior to the presentation. Let me know which paper you choose. Note that some papers are in postscript format -- you may need to get a postscript reader for these.

Before the presentation, everyone in the class must read the paper so we may have a fruitful discussion. Thus the target audience of the presentation is people who already have learned something about the topic.

Guidelines and evaluation

The presentation should use slides and be about 20 minutes long, and allow for an additional 10 minutes of discussion (either at the end, or during the talk). As a rule of thumb, 1 minute of presentation means about 1 slide. The presentation should address and lead the class in a discussion of the main points of the article. In particular, talk about:

The focus of the presentation should be on presenting the work, but do spend a little time giving your critique of the work as appropriate.

Consider this advice from Charles Elkan on notes on giving a research talk

Your grade will be based on the clarity and quality of your presentation, how well you lead the discussion and are able to answer any questions that come up.

Topic: Decision trees (February 3rd; Jonathan)

We will be reading the Oates and Jensen paper.

Topic: Neural networks (February 12; George)

We will be reading the Caruana, Lawrence, and Giles paper.

Topic: Learning theory (February 24)

Topic: Bayesian learning (March 3; Clint)

We will be reading the Domingos and Pazzani paper.

Topic: Instance-based learning (March 26; Daniel)

We will be reading the Moore and Lee paper.

Topic: Support vector machines (April 7; Casey)

We will be reading the Joachims paper.

Topic: Unsupervised learning (April 9; Nick)

We will be reading the Ester, Kriegel, Sander, and Xu paper.

Topic: Boosting (April 21; Ryan)

We will be reading the Viola and Jones paper.

Copyright © 2010 Greg Hamerly.
Computer Science Department
Baylor University

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