CEE 616: Probabilistic Machine Learning
UMass Amherst, Fall 2025
Canvas Lecture Videos Gradescope Notebooks
Schedule
Module 1 Foundations
- Tue, Sep 2
- LECTURE 1AIntroduction
- Thu, Sep 4
- LECTURE 1BProbability
- Tue, Sep 9
-
- LECTURE 1CStatistics
- PROBLEM SET 1PDF, LaTeX, Solution (Due 9/25)
- Thu, Sep 11
- LECTURE 1DDecision and Information Theories
- Tue, Sep 16
- LECTURE 1ELinear Algebra
- Thu, Sep 18
- LECTURE 1FOptimization
Module 2 Linear Methods
- Tue, Sep 23
- LECTURE 2ALinear Discriminant Analysis
- Thu, Sep 25
- LECTURE 2BLogistic Regression
- Thu, Oct 2
- LECTURE 2CLinear Regression (OLS/WLS)
- Tue, Oct 7
- LECTURE 2EGeneralized Linear Models
- NOTEBOOKGLM - Poisson and Logistic
- Thu, Oct 9
-
- LECTURE 2DRidge and Lasso Regression
- PROBLEM SET 2PDF, LaTeX; Due: 10/16
- NOTEBOOKRidge and Lasso
Module 3 Deep Neural Networks
- Thu, Oct 16
-
- LECTURE 3ANN for Structured Data I
- PROBLEM SET 3PDF, LaTeX
- NOTEBOOKXOR demonstration
- Tue, Oct 21
- LECTURE 3BNN for Structured Data II
- NOTEBOOKANN-Exploration
- Thu, Oct 23
- LECTURE 3CNN for Images
- NOTEBOOKCNN-Exploration
- Tue, Oct 28
- LECTURE 3DNN for Sequences
- NOTEBOOKRNN-Exploration
Module 4 Nonparametric Methods
- Thu, Oct 30
-
- LECTURE 4ANonparametric Methods
- PROBLEM SET 4PDF, LaTeX
- Thu, Nov 6
- LECTURE 4BGaussian Processes
- Thu, Nov 13
- LECTURE 4CSupport Vector Machines
Tue, Nov 18: LECTURE 4DTrees and Ensemble Methods
Module 5 Unsupervised Learning
- Tue, Dec 2
- LECTURE 5APrincipal Components Analysis
- Thu, Dec 4
- LECTURE 5BFactor Analysis and Autoencoders