UM Statistical Learning

Rackham Interdisciplinary Workshop



Fall Workshop Schedule

The workshop meets on Mondays at 2:00-3:30pm in the Walker Room on the 5th floor of Haven Hall Please email us to reserve a time-slot if you wish to present work.

  • Oct 16: Introduction to Bayesian Analysis Part I (led by Kevin McAlister)
    • Readings TBA
  • Oct 30: Introduction to Bayesian Analysis Part II (led by Kevin McCallister and Diogo Ferrari)
    • Readings TBA
  • Nov 13: Blake Milner (job market paper, title TBA)
  • Nov 27: Siminar: MCMC Estimation and Implementation in JAGS
  • Dec 11: Bayesian non-parametric Models (led by Kevin McCallister and Diogo Ferrari)
    • Readings TBA

Winter Workshop Schedule

  • Jan 8: Variational Inference
    • Readings TBA
  • Jan 22: TBA
  • Feb 5: TBA
  • Feb 19: TBA
  • Mar 5: TBA
  • Mar 19: TBA
  • Apr 6: TBA
  • Apr 16: TBA

About

Statistical Learning refers to the application and development of statistical frameworks to problems relating to pattern recognition. It is a growing area of research in Statistics and Computer Science. Recognizing patterns in a set of data, either for prediction or inference purposes, lies at the core of scientific pursuits. Not surprisingly, it has influenced the development of scientific investigation in many fields as Economics, Political Science, Finance, Sociology, Biostatistics, Medicine, Marketing, and many other areas.

Some applications of statistical learning involve inference about the flow of information in a network, classifying unseen text data based on previously classified observations (supervised learning and classification), automatically identifying data with similar underlying features (unsupervised learning and clustering), and so on.

Our goal with this interdisciplinary workshop is to provide a forum for researchers and graduate students share how they have applied statistical learning techniques to solve problems in their disciplines. We hope this workshop can provide an environment that facilitates cross-disciplinary information sharing, aiding graduate students, faculty, and researchers in the pursuit of tools to solve problems in their respective disciplines.


Sign up for our email list

Please fill out this form to sign up for our email list. For any questions about the workshop, please contact the graduate student coordinator Diogo Ferrari.

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Resources

Our workshop will be using Elements of Statistical Learning (ESL) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, "Machine Learning: a Probabilistic Perspective," by Kevin Patrick Murphy, and "Deep Learning" by Goodfellow and Yoshua Bengio and Aaron Courville for our review weeks. For those new to statistical learning, an introductory text, Introduction to Statistical Learning (ISL) can be found for free here. In addition, lecture videos corresponding to each chapter in the book can be found here. Other material used in the workshop may vary from week to week. They are described in the schedule.

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