Here is a rough outline of the course. I'm not really aiming for a coherent sequence - rather, I hope we can discuss a variety of topics that you might not encounter in your regular classes, and that I hope you find interesting. We will meet Mondays and Wednesdays 1pm-3pm in 903 SSW. During the first class session we will discuss course work, projects, etc.

I am using Professor Heyde's office on the 10th floor. Generally I will be there before and after class for an hour or so, but email me in advance if you want to be sure I'll be there.

Thanks for an enjoyable month! I should be able to assign letter grades tomorrow. Have a good summer! D.

• June 5th.
  An Overview of Data Mining.
  
  • We'll start with an overview of the field of data mining and discuss a few specific data mining algorithms that give a flavor of the way data miners think.
    

• June 7th and 12th.
  Logistic and Tobit Regression.
  Regularized Logistic Regression with Application to Text Categorization
  Brief Notes on Kernel Methods
  
  
  • This class will take a close look at a statistical warhorse that ought to get more attention in data mining. Logistic regression is a particular regression model for binary responses that scales well to ultra-high dimensional applications, predicts as well as many of its competitors, and offers a transparency advantage over many machine learning algorithms.
    

• June 14th.
  Brief Notes on Confounding.
  Causal Inference in Observational Studies.
  Post-marketing Surveillance of Drug Safety (June 19th).
  
  
  • Making causal inferences from observational data seems like the quintessential data mining activity! We will explore the "propensity scoring approach" and related ideas. We will also look at an important application area - drug safety - where these ideas are relevant.
    

• June 19th.
  An Introduction to Probabilistic Graphical Models
  
  
  • Probabilistic graphical models uses graphs to represent multivariate probabilistic models. This idea is old but the last decade or so has seen extraordinary developments, both in the appications of graphical models and in the associated rather elegant theory.
    

• June 19th.
  Locating Users in Wireless Networks
  Causal Inference in "Broken" Clinical Trials
  
  
  • This class will explore two interesting applications of probabilistic graphical models.
    

• June 21st.
  Some Graphical Models Theory
  
  
  • We'll take a detailed look at some selected important results in the theory of graphical models.
    

• June 26th.
  Text Sequence Modeling
  
  
  • The last few years have seen some interesting developments in models for sequences of words. We'll take a look at some of this focusing in particular on hidden Markov models, maximum entropy Markov models, and conditional random fields. Graphical models have played an important role in these developments. Some of the same ideas have work for biological sequences but I don't know much about this.
    

• June 28th.
  ???
  
  
  • I'd like to talk about the role of dropouts in clinical trials. Or, we might talk about functional decision trees. Lets see how things go.