EE 381V Statistical Machine Learning [Spring 2025]
Lectures: MW 1:30-3:00pm, ECJ 1.312
Teaching Assistant: Jinze Zhao (jz24694@utexas.edu)
References
Primary source:
S. Shalev-Shwartz and S. Ben-David, 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, Probabilistic Machine Learning: Introduction and
Probabilistic Machine Learning: Advanced Topics, MIT Press, 2022-23.
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 bias-complexity tradeoff; the Vapnik-Chervonenkis dimension; Rademacher complexities; PAC-Bayes; 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. infinite-size classes
- the bias-complexity tradeoff
- the Vapnik-Chervonenkis (VC) dimension
- Rademacher complexities
- PAC-Bayes
- 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, 471-6259, http://www.utexas.edu/diversity/ddce/ssd/.
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