EE 381V Statistical Machine Learning [Spring 2023]
Lectures: MW 9:0010:30a, ART 1.120
Teaching Assistant: Pedram Akbarian (akbarian@utexas.edu)
References
Primary source:
S. ShalevShwartz and S. BenDavid, Understanding Machine Learning, Cambridge University Press, 2014.
Additional reference:
T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2009 (2nd edition).
K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
Grading (tentative)
Homework policy: Homeworks are to be submitted at the
beginning of the class when they are due. You may discuss homework problems
with other students, but must submit your own independent solution. Late
homework assignments will not be accepted.
Prerequisites: EE381J Probability and Stochastic Processes I (or equivalent) and background in linear algebra (e.g., at the level of EE381K Convex Optimization) are required.
Course description:
Introduction to the theoretical underpinnings of machine learning and the derivations that transform those foundations into practical algorithms. Topics include empirical risk minimization and the probably approximately correct (PAC) learning framework; the biascomplexity tradeoff; the VapnikChervonenkis dimension; Rademacher complexities; PACBayes; linear predictors; boosting; model selection and validation; convex learning problems; support vector machines and kernel methods; decision trees; clustering; dimensionality reduction; generative models; and variational Bayesian methods.
Course outline (tentative):
 Introduction and Foundations
 the statistical learning framework; empirical risk minimization
 probably approximately correct (PAC) learning; finite vs. infinitesize classes
 the biascomplexity tradeoff
 the VapnikChervonenkis (VC) dimension
 Rademacher complexities
 PACBayes
 From Theory to Algorithms
 linear prediction; boosting
 model selection and validation
 convex learning problems; regularization and stability
 support vector machines; kernel methods
 multiclass, ranking and complex prediction problems
 decision trees; nearest neighbors
 Beyond the PAC Learning Model
 clustering
 dimensionality reduction
 generative models
 variational Bayes methods
 feature selection and generation
Notice for students with disabilities
Students with disabilities may request appropriate academic accommodations from the Division of Diversity and Community Engagement, Services for Students with Disabilities, 4716259, http://www.utexas.edu/diversity/ddce/ssd/.
Emergency instructions: Classroom evacuation for students
All occupants of university buildings are required to evacuate a building when a fire alarm and/or an official announcement is made indicating a potentially dangerous situation within the building. Familiarize yourself with all exit doors of each classroom and building you may occupy. Remember that the nearest exit door may not be the one you used when entering the building. If you require assistance in evacuation, inform your instructor in writing during the first week of class
For evacuation in your classroom or building:
 Follow the instructions of faculty and teaching staff.
 Exit in an orderly fashion and assemble outside.
 Do not reenter a building unless given instructions by emergency personnel.
