Class Material

Indices

Sequentially


Week 1

Lecture 1: Introduction

Slides and Notes, Video

Lab 1: Bayes Theorem and Python Tech

Material, Video


Week 2

Lecture 2: Probability, Sampling, and the Laws

Slides and Notes, Video

Lecture 3: From Monte Carlo to Frequentism

Slides and Notes

Lab2: Frequentism, Bootstrap, and MLE

Material


Week 3

Lecture 4: MLE, Sampling, and Learning

Slides and Notes

Lecture 5: Regression, AIC, Info. Theory

Slides and Notes

Lab 3: Generating regression data, fitting it, training, and testing

Material


Week 4

Lecture 6: Risk, AIC, Info. Theory

Slides and Notes

Lecture 7: From Entropy to Bayes

Slides and Notes

Lab 4: Bayesian Quantities in the Globe Model

Material


Week 5

Lecture 8: Bayes and Sampling

Slides and Notes

Lecture 9: Bayes and Sampling

Slides and Notes

Lab 5: Logistic Regression and Sundry Bayesian

Material


Week 6

Lecture 10: Sampling and Gradient Descent

Slides and Notes

Lab 6: Sampling and PyTorch

Material


Week 7

Lecture 11: Gradient Descent and Neural Networks

Slides and Notes

Lecture 12: Non Linear Approximation to Classification

Slides and Notes

Lab 7: PyTorch

To be linked


Week 8

Lecture 13: Classification, Mixtures, and EM

Slides and Notes

Lecture 14: EM and Hierarchcal models

Slides and Notes

Lab 8: EM and Hierarchicals

Material


Week 9

Lecture 15: MCMC

Slides and Notes

Lecture 16: MCMC and Gibbs

Slides and Notes

Lab9: Sampling and Pymc3

Material


Week10

Lecture 17: Gibbs, Augmentation, and HMC

Slides and Notes

Lecture 18: HMC, and Formal tests

Slides and Notes

Lab10: Jacobians and Tumors

Material


Lecture 19: NUTS, Formal tests, and Hierarchicals

Slides and Notes