STAT 535

Foundations of Machine Learning
STAT 535 Autumn Quarter 2021

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Handouts/Course notes

A guide to the course notes. On this page I will post the course notes that will be used during each lecture. These will be slides, and will be posted before the lecture. After each lecture, the annotated slides will be posted on Canvas. One "Lecture" file may be used for more than one actual lecture. To eliminate confusion, the unannotated notes posted here will be titled Lecture I, Lecture II, etc, while the annotated notes after each lecture, will be titled l1-oct1, l2-oct3, etc. Additional materials not used in class will be posted here and linked to from Canvas.

STAT 535 Course Information
Lecture 0 Overview of Machine Learnng
Lecture I Examples of predictors
Lecture II Prediction -- Basic concepts
Lecture II.1 Bias and variance for Kernel Regression
Lecture III Neural networks architectures
Lecture IV-I Training predictors -- part I
Lecture IV-I.1 Simple analysis of gradient descent
Lecture IV-II Training predictors -- part II
Lecture V Model selection
Lecture V notes
Tutorial on Practical Prediction Theory for Classification by John Langford
Lecture VI Support Vector Machines
Lecture VI-2 Random Fourier Features
Lecture VII Double descent
a simple example
Lecture VIII Clustering -- Parametric loss based and model based clustering. See also Lecture VII--Part III, 2020 below
Previews/Unedited course notes
can be found on the Fall 2020 course web site
Lecture VII, 2020 Clustering -- Part III, Cluster validation, distances between clusterings (t.b.updated)