Notes & slides
Materials
Every public set of lecture notes, slides, and teaching materials I have written, collected in one place. All of it is free to download and use for learning.
Lecture notes
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Regression, from least squares to neural tangent kernels
Notes accompanying the recorded lecture: linear regression, regularization, kernels, and a first look at neural tangent kernels.
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A recap on convex optimization
Notes accompanying the recorded lecture: convex sets and functions, duality, and the optimization tools used throughout machine learning.
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An Elementary Introduction to Quantum Computing
The basics of quantum computation using only linear algebra over the real numbers. No prior quantum mechanics or complex analysis required.
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Algorithm Validation via Information Theory
How can you tell whether your algorithm is learning correctly from your data? A method based on information theory, originally proposed by Professor Emeritus Joachim Buhmann.
Notes (PDF) · Slides 1 · Slides 2 · Slides 3
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A Dog, a Vegan Flea, and the EM Algorithm
A simple but rigorous derivation of the expectation-maximization algorithm using a two-dimensional dog and a vegan flea.
Workshop slides
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The Essentials of Machine and Deep Learning
A half-day workshop introducing classification with machine and deep learning. Only basic Python is required. The Docker images contain ready-to-run environments.
ML slides (PDF) · DL slides (PDF) · ML Docker image · DL Docker image
Beyond PDFs
For recorded lectures, see the teaching portfolio; for the click-through explainers (PCA, neural networks, VAEs, reinforcement learning), see the interactive visualizations.