## Indices

- Lectures and Labs (along with readings for these lectures)
- Videos
- Homework
- Topics Index
- Terms Glossary

## Sequentially

### Week 1

Lecture 1: **Introduction**

Lecture 2: **Probability and Distributions and Frequentism**

Lab1: **Frequentism**

### Week 2

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

Lecture 4: **Sampling**

Lab2: **Python, Math, and Stratification**

### Week 3

Lecture 5: **Machine Learning**

Lecture 6: **Machine Learning, contd, Gradient Descent**

Lab3: **PyTorch, Regressions, and Artificial Neural Networks**

### Week 4

Lecture 7: **Machine Learning and Backpropagation**

Lecture 8: **Neural Nets and Information Theory**

Lab4: **PyTorch and Artificial Neural Networks contd**

### Week 5

Lecture 9: **Information Theory, Deviance, and Global Optimization**

Lecture 10: **Annealing, Markov, and Metropolis**

Lab5: **Simulated Annealing and the TSP**

### Week 6

Lecture 11: **Metropolis To Bayes**

Lecture 12: **Bayes**

Lab 6: **Sampling and Pymc3**

Lecture 13: **More Bayes**

Lecture 14: **Convergence and Gibbs**

Lab7: **Sampling and Hierarchicals**

Lecture 15: **Linear Regression**

Lecture 16: **Gaussian Processes**

Lab8: **Regression and GPs**

Lecture 17: **Augmentation and Slice and HMC**

Lecture 18: **HMC, Normal Normal Hierarchical**

Lab9: **Normal-Normal Hierarchicals**

Lecture 19: **Posterior Predictive Checks and GLMs**

Lecture 20: **Decisions, Model Comparison, and GLMs**

Lab 10: **Prosocial Chimps Bernoulli glm**

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

Lecture 22: **Workflow and Mixtures**

Lab11: **Mixtures and log-sum-exp marginals**

Lecture 23: **EM and Mixtures**

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**

Lecture 26: **Recap**