SELECTED ONLINE TUTORIALS

5/19-20/22 "Manifold Learning for Real Data" at the Fields Institute Focus Program on Data Science, Approximation Theory, and Harmonic Analysis

• Abstract and Video Lecture 1, Lecture 2, Lecture 3 and Plenary talk

• Lecture slides (annotated) from Lecture 1 and Lectures 2-3

• Lecture slides with additional definitions and notes

Course module (slides) Cluster validation Level: Graduate/advanced undergraduate

SPRING 2023

STAT 391 Statistics for Data Science

STAT 527 Non-parametric Statistics (Pages under construction)

REGULAR OFFERINGS

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