SELECTED ONLINE TUTORIALS
5/1920/22 "Manifold Learning for Real Data" at the Fields Institute Focus Program on Data Science, Approximation Theory, and Harmonic Analysis
SPRING 2022
STAT
391 Statistics for Data Science
STAT
572 Preparation for Research Prelim
REGULAR OFFERINGS
STAT 535
Foundations of Machine Learning (Fall 2021)
STAT 538
Advanced Machine Learning
CSE 547/STAT 548
Machine Learning for Big Data
STAT 534 Statistical computing: Basic data structures and algorithms (Spring 2019)
STAT 498/CSSS 594
Statistics and Philosophy of voting with Elena Erosheva and Conor MayoWilson
ONE TIME OFFERINGS and old classes
STAT
544 Bayesian Inference
STAT 535
Statistical Learning: Modeling, Prediction and Computing (Fall 2011) Part I: Graphical probability models and unsupervised learning (formerly Statistical Computing)
STAT 538
Statistical Learning: Modeling, Prediction and Computing (Winter 2010) Part II: Supervised Learning and Optimization (formerly Statistical Computing)
STAT 539 Machine Learning/Big Data Project
STAT
391 Probability and Statistics for Computer
Science
STAT 593C/EE 546 Sparse representations: Theory, Algorithms and Applications (with Maryam Fazel
), Spring 2010
STAT 591/EE 596 Modern methods of machine learning: multiway classification, preferences, intransitivity (with Jeff
Bilmes), Autumn 2006
STAT 593 B Probabilistic reasoning
with graphical models (with Jeff
Bilmes), Spring 2005
STAT
592 B/CSE 590 MM Classic methods of Machine Learning (with Alejandro Murua), Winter 2004
STAT 592 C Winter 2002 Convex Analysis Methods in Statistical Inference (with Jeff
Bilmes and Thomas Richardson)
STAT
593 C Information Theory, Statistics and Machine Learning Spring
2001 (with Jeff
Bilmes)
MATH 498 Mathematics/ACMS Undergraduate Seminar
