Class Material

Indices

Sequentially


Week 1

Lecture 1: Introduction

Slides and Notes

Lecture 2: Probability and Distributions and Frequentism

Slides and Notes

Lab1: Frequentism

Material


Week 2

Lecture 3: Law of Large Numbers, CLT, and Monte Carlo

Slides, Notes and Readings

Lecture 4: Sampling

Slides, Notes and Readings

Lab2: Python, Math, and Stratification

Material


Week 3

Lecture 5: Machine Learning

Slides, Notes and Readings

Lecture 6: Machine Learning, contd, Gradient Descent

Slides, Notes and Readings

Lab3: PyTorch, Regressions, and Artificial Neural Networks

Material


Week 4

Lecture 7: Machine Learning and Backpropagation

Slides, Notes and Readings

Lecture 8: Neural Nets and Information Theory

Slides, Notes and Readings

Lab4: PyTorch and Artificial Neural Networks contd

Material


Week 5

Lecture 9: Information Theory, Deviance, and Global Optimization

Slides, Notes and Readings

Lecture 10: Annealing, Markov, and Metropolis

Slides, Notes and Readings

Lab5: Simulated Annealing and the TSP

Material


Week 6

Lecture 11: Metropolis To Bayes

Slides, Notes and Readings

Lecture 12: Bayes

Slides, Notes and Readings

Lab 6: Sampling and Pymc3

Material


Lecture 13: More Bayes

Slides, Notes and Readings

Lecture 14: Convergence and Gibbs

Slides, Notes and Readings

Lab7: Sampling and Hierarchicals

Material


Lecture 15: Linear Regression

Slides, Notes and Readings

Lecture 16: Gaussian Processes

Slides, Notes and Readings

Lab8: Regression and GPs

Material


Lecture 17: Augmentation and Slice and HMC

Slides, Notes and Readings

Lecture 18: HMC, Normal Normal Hierarchical

Slides, Notes and Readings

Lab9: Normal-Normal Hierarchicals

Material


Lecture 19: Posterior Predictive Checks and GLMs

Slides, Notes and Readings

Lecture 20: Decisions, Model Comparison, and GLMs

Slides, Notes and Readings

Lab 10: Prosocial Chimps Bernoulli glm

Notes and Material


Lecture 21: Decisions, Model Comparison, and GLMs, Ensembles, Workflow

Slides, Notes and Readings

Lecture 22: Workflow and Mixtures

Slides, Notes and Readings

Lab11: Mixtures and log-sum-exp marginals

Notes and Material


Lecture 23: EM and Mixtures

Slides, Notes and Readings

Lecture 24: Expectation Maximization and Variational Inference Slides, Notes, and Readings

Lab12: Correlations and Mixtures and ADVI Notes and Material


Lecture 25: Variational Bayes and Generative Models

Slides, Notes and Readings

Lecture 26: Recap

Slides, Notes and Readings