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Fall 2017


Introduction to Statistical Machine Learning, Stat 413, Fall 2017.

Tuesday and Thursday 4:00 - 5:15pm

This course is an introduction to concepts, methods, and best practices in statistical machine learning. Topics covered include regularized regression, classification, kernels, dimension reduction, clustering, trees, and ensemble learning. Emphasis will be placed on applied data analysis and computation. Prerequisites: Regression and Programming (R, Matlab, or Python). Recommended Prerequisites: Linear Algebra.



Statistical Machine Learning, Stat 613, Fall 2017.

Tuesday and Thursday 10:50am - 12:05pm

This course is an advanced survey of statistical machine learning theory and methods. Emphasis will be placed methodological, theoretical, and computational aspects of tools such as regularized regression, classification, kernels, dimension reduction, clustering, graphical models, trees, and ensemble learning.


Spring 2017


Special Topics in Statistical Machine Learning, Stat 620, Spring 2017.

Tuesday and Thursday 1:00 - 2:15pm.

Fall 2016


Data Mining and Statistical Learning, Stat 444/640, Fall 2016.

Tuesday and Thursday 4:00 - 5:15pm. HRZ 210.

Fall 2015


Data Mining and Statistical Learning, Stat 444/640, Fall 2015.

Tuesday and Thursday 4:00 - 5:15pm. HRZ 210.

Spring 2015


Probability & Statistics, Stat 310, Spring 2015.

Tuesday and Thursday, 1:00pm - 2:15pm.

Fall 2014


Data Mining and Statistical Learning, Stat 444/640, Fall 2014.

Tuesday and Thursday 4:00 - 5:15pm. HRZ 210.


Big Data Analytics, Stat 699, Fall 2014.

Mondays 1:00 - 2:30pm. DH 1049.

Spring 2014


Introduction to Mathematical Probability & Statistics, Stat 310, Spring 2014.

Tuesday and Thursday, 1:00pm - 2:15pm.

Fall 2013


Data Mining and Statistical Learning, Stat 640, Fall 2013.

Monday and Wednesday 2:30 - 3:45pm.

Spring 2013


Introduction to Mathematical Probability & Statistics, Stat 310, Spring 2013.

Tuesday and Thursday, 1:00pm - 2:15pm.

Pre-requisites: Math 101 & 102 (single variable calculus). Recommended: Math 212 (multi-variable calculus).

Fall 2012


Data Mining and Statistical Learning, Stat 640, Fall 2012.

Monday and Wednesday 2:30 - 3:45pm.

Fall 2011


Data Mining and Statistical Learning, Stat 640, Fall 2011.

Monday and Wednesday 2:30 - 3:45pm.

Spring 2011


Statistical Learning: High-Dimensional Data Stat 699, Spring 2011.

Mondays, 1 - 2pm.

Fall 2010


Random Matrix Theory, Math/CAAM/Stat 498/698, Fall 2010, (team teacher).

Resources for Instructors

  • Introductory Statistics: Database of online problems and solutions.
    Hosted on Quadbase, this is a curated list of publicly available probability and statistics questions and solutions with tags on concepts and difficulty levels. Instructors should email gallen@rice.edu for a link to this list.


  • Statistical Learning: Data Mining Competitions.
    For instructors interested in hosting a data mining competition in their course (undergraduate or graduate level), here are some resources that may be helpful. Please email gallen@rice.edu to request this material.
    • Six competition data sets (Jester, Toy images, MovieLens, Human Activity, Dating Profile Recommender, and ECoG Speech Decoding data). These include splits into the training, query, and test sets as well as supplementary information.
    • Competition descriptions and rules.
    • Grading rubrics (at undergraduate and graduate levels).
    • Example R code and competition benchmarks.