[ABE-L] Seminar on Statistical Learning at Insper

[Hedibert Lopes]

Bom dia pessoal. Quem estiver interessado no novo ciclo de seminarios,
dessa vez sobre Statistical Learning:

http://hedibert.org/current-teaching/#tab-statistical-learning

Por favor me avise para eu comecar a organizar uma lista de participantes.

Abs,
Hedibert



*Bayesian Statistical Learning: Readings in Statistics and Econometrics*

Organizer: Hedibert Freitas Lopes
<http://hedibert.org/current-teaching/www.hedibert.org> and Paulo Marques

Email: hedibertFL at insper.edu.br

In this Second Readings in Statistics and Econometrics we will study and
discuss, through a series of well established papers, the broad topic of
Statistical Learning with an emphasis on its natural Bayesian solutions.
The 5 lectures and 8 seminars will take place on Fridays between 10am and
12pm from January 29th to April 8th 2016. Paulo and I will give lectures
discussing traditional Statistical Learning techniques, alternated with
seminars given by the participants on papers presenting Bayesian
counterparts to the techniques discussed in the lectures.

*Outline of the meetings (5 lectures and 8 seminars)*

   1. Januaryt 29th: Lecture 1 on k-nearest neighors (k-NN)
   2. February 2nd: Seminars
      1. Seminar 1: Holmes and Adams (2002,2003)
      2. Seminar 2: Cucala, Marin, Robert and Titterington (2009)
   3. February 12th: Lecture 2 on LASSO regularization
   4. February 19th: Seminars
      1. Seminar 3: Griffin and Brown (2010,2012,2014)
      2. Seminar 4: Polson, Scott and Windle (2014)
   5. February 26th: Lecture 3 on Random Forests
   6. March 4th: Seminar
      1. Seminar 5: Chipman, George and McCulloch, (2010)
   7. March 11th: Lecture 4 on Supporting Vector Machines
   8. March 18th: Seminar
      1. Seminar 6: Tipping (2001)
      2. Seminar 7: Polson and Scott (2011)
   9. April 1st: Lecture 5 on k-means Clustering
   10. April 8th: Seminar
      1. Seminar 8:Kulis and Jordan (2012)

*Books and papers*

   1. An Introduction to Statistical Learning (James, Witten, Hastie and
   Tibshrani)
   2. Applied Predictive Modeling (Kuhn and Johnson)
   3. Bayesian Reasoning and Machine Learning (Barber)
   4. The Elements of Statistical Learning (Hastie, Tibshrani and Friedman)
   5. Machine Learning: A Probabilistic Perspective (Murphy)
   6. Pattern Recognition and Machine Learning (Bishop)
   7. Pattern Classification (Duda, Hart and Stork)
   8. Probability and Measure (Billingsley)
   9. Probability and Measure Theory (Ash)
   10. Optimal Statistical Decisions (DeGroot)
   11. Theory of Statistics (Schervish)
   12. Principles of Uncertainty (Kadane)

*Papers*

   1. Chipman, George and McCulloch (2010) BART: Bayesian Additive and
   Regression Trees. AOAS, 4, 266-298.
   2. Cucala, Marin, Robert and Titterington (2009) A Bayesian Reassessment
   of Nearest-Neighbor Classification. JASA, 104, 263-273.
   3. Griffin and Brown (2010) Inference with normal-gamma prior
   distributions in regression prob- lems. BA, 5, 171-188.
   4. Griffin and Brown (2012) Structuring shrinkage: some correlated
   priors for regression. Biometrika, 99, 481-487.
   5. Griffin and Brown (2013) Some priors for sparse regression modelling.
   BA, 8, 691-702.
   6. Holmes and Adams (2002) A Probabilistic Nearest Neighbour Method for
   Statistical Pattern Recognition. JRSS-B, 64, 295-306.
   7. Holmes and Adams (2003) Likelihood Inference in Nearest-Neighbour
   Classification Models. Biometrika, 90, 99-112.
   8. Kulis and Jordan (2012) Revisiting k-means: New Algorithms via
   Bayesian Nonparametrics. Proceedings of the 29th International Conference
   on Machine Learning, Edinburgh, Scotland.
   9. Polson and Scott (2011) Data Augmentation for Support Vector
   Machines. BA, 6, 1-24.
   10. Polson, Scott and Windle (2014) The Bayesian Bridge. JRSS-B, 76,
   713-733.
   11. Tipping (2001) Sparse Bayesian learning and the Relevance Vector
   Machine. JMLR, 1, 211-244.
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