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 9: Bayesian Analysis Part I
    • Readings TBA
  • Oct 30: Bayesian Analysis Part II
    • Readings TBA
  • Nov 13: Blake Miller : The Limits of Commercialized Censorship in China
  • Nov 27: Seminar: MCMC Estimation and Implementation in JAGS
  • - Introduction
    • Chib, S., & Greenberg, E., Understanding the metropolis-hastings algorithm, The American Statistician, 49(4), 327 (1995).
    • Andrieu, C., De Freitas, N., Doucet, A., & Jordan, M. I., An introduction to mcmc for machine learning, Machine learning, 50(1-2), 5–43 (2003).
    • Neal, R., Probabilistic inference using mcmc methods, Toronto: University of Toronto (1993).
    - Application
    • Plummer, M., Jags version 4.3.0 user manual, (2017). (webpage)
    - Advanced
    • Meyn, S. P., & Tweedie, R. L., Markov chains and stochastic stability (2012), : Springer Science \& Business Media.

Winter Workshop Schedule

  • Jan 8: Variational Inference
    • Readings TBA
  • Jan 22: TBA
  • Feb 5 Timothy D. Johnson : Hamiltonian Monte Carlo and related techniques
  • Feb 19: Patrick Wu (TBA)
  • Mar 5: Ceren Budak (Topic TBA)
  • Mar 19: TBA
  • Apr 2 Jeff_Gill: TBA
  • Apr 16: TBA


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.

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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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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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