Indices
- Lectures and Labs (along with readings for these lectures)
- Videos
- Homework
- Topics Index
- Terms Glossary
Sequentially
Week 1
Lecture 1: Introduction
Lab 1: Bayes Theorem and Python Tech
Week 2
Lecture 2: Probability, Sampling, and the Laws
Lecture 3: From Monte Carlo to Frequentism
Lab2: Frequentism, Bootstrap, and MLE
Week 3
Lecture 4: MLE, Sampling, and Learning
Lecture 5: Regression, AIC, Info. Theory
Lab 3: Generating regression data, fitting it, training, and testing
Week 4
Lecture 6: Risk, AIC, Info. Theory
Lecture 7: From Entropy to Bayes
Lab 4: Bayesian Quantities in the Globe Model
Week 5
Lecture 8: Bayes and Sampling
Lecture 9: Bayes and Sampling
Lab 5: Logistic Regression and Sundry Bayesian
Week 6
Lecture 10: Sampling and Gradient Descent
Lab 6: Sampling and PyTorch
Week 7
Lecture 11: Gradient Descent and Neural Networks
Lecture 12: Non Linear Approximation to Classification
Lab 7: PyTorch
To be linked
Week 8
Lecture 13: Classification, Mixtures, and EM
Lecture 14: EM and Hierarchcal models
Lab 8: EM and Hierarchicals
Week 9
Lecture 15: MCMC
Lecture 16: MCMC and Gibbs
Lab9: Sampling and Pymc3
Week10
Lecture 17: Gibbs, Augmentation, and HMC
Lecture 18: HMC, and Formal tests
Lab10: Jacobians and Tumors
Week11
Lecture 19: NUTS, Formal tests, and Hierarchicals
Lecture 20: Regression, GLMs, and model specification
Lab11: Gelman Schools Hierarchical and Prosocial Chimps GLM
Week 12
Lecture 21: From Hierarchical GLMs to Gaussian Processes
Week 13
Lecture 22: Decisions and Model Comparison
Lecture 23: Cross-Validation, Priors, and Workflow
Lab12: GLM and Workflow
Lecture 24: Variational Inference
Lecture 25: Variational inference and Mixtures
Lecture 26: Wrapup