## 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**

Lecture 19: **NUTS, Formal tests, and Hierarchicals**