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 1; Chiam)

We will read the paper "Decision Trees for Hierarchical Multi-label Classification: A Case Study in Functional Genomics" by Blockeel, Schietgat, Struyf, Dzeroski, and Clare.

Topic: Neural networks (February 15; Yilan)

We will read the paper "Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping" by Caruana, Lawrence, and Giles.

Topic: Learning theory (March 31; Radu)

We'll read the paper "A PAC-style model for learning from labeled and unlabeled data" by Balcan and Blum.

Topic: Bayesian learning (March 14; Yanxin)

We will read the paper "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier" by Domingos and Pazzani.

Topic: Instance-based learning (April 21; Hao)

We will read "A learning framework for nearest neighbor search" by Cayton and Dasgupta.

Topic: Support vector machines

Topic: Unsupervised learning

Topic: Boosting

Copyright © 2011 Greg Hamerly.
Computer Science Department
Baylor University

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