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
Course module (slides) Cluster validation Level: Graduate/advanced undergraduate
SPRING 2023
STAT
391 Statistics for Data Science
STAT
527 Nonparametric Statistics (Pages under construction)
REGULAR OFFERINGS
STAT 498/CSSS 594
Statistics and Philosophy of voting with Elena Erosheva and Conor MayoWilson (Fall 2022)
STAT 535
Foundations of Machine Learning
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
572 Preparation for Research Prelim
ONE TIME OFFERINGS and older 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
