| layout | title |
|---|---|
page |
Notebooks and videos |
| Date | Topic | Reading | Video | Notebook | Script | Lecture recording | |
|---|---|---|---|---|---|---|---|
| 0 | 09-09 | Welcome! | [prismia] | [zoom] | |||
| 1 | 09-11 | Programming in Julia | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 2 | 09-14 | Linear Algebra Practice | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 3 | 09-16 | Eigenvectors and eigenvalues | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 4 | 09-18 | Multivariable Calculus | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 5 | 09-21 | Matrix differentiation | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 6 | 09-23 | Machine arithmetic, numerical error | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 7 | 09-25 | Pseudorandom numbers and automatic differentiation | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 8 | 09-28 | Gradient descent algorithms | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 9 | 09-30 | Probability Review | [DG] [youtube] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 10 | 10-02 | Bayes' theorem and conditional expectation | [DG] [youtube] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 11 | 10-05 | Common Distributions and the Central Limit Theorem | [DG] [youtube] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 12 | 10-07 | Simulation techniques and introduction to statistics | [DG] [youtube] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 13 | 10-09 | Kernel density estimation | [DG] [youtube] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 14 | 10-14 | Point estimation and confidence intervals | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 15 | 10-16 | Empirical CDF convergence and bootstrapping | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 16 | 10-19 | Maximum likelihood estimation and hypothesis testing | [DG] [youtube] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 17 | 10-21 | Statistical Learning Theory | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 18 | 10-23 | Linear Regression and Quadratic Discriminant Analysis | [DG][DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 19 | 10-26 | Likelihood ratio classification | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 20 | 10-28 | Generative models (QDA, LDA, Naive Bayes) | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 21 | 10-30 | Logistic Regression | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 22 | 11-02 | Support Vector Machines | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| - | 11-04 | Review Day | [zoom] | ||||
| 23 | 11-06 | Support Vector Machines (II) | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 24 | 11-09 | Decision Trees | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 25 | 11-11 | Ensemble Methods | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 26 | 11-13 | Neural Networks | [DG] [3B1B] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 27 | 11-16 | Neural Networks (II) | [DG] [3B1B] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 28 | 11-18 | Dimension Reduction | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| - | 11-20 | Review | - | - | - | [prismia] | [zoom] |
| 29 | 11-13 | Bayesian Statistics and Markov Chains | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| - | 11-25 | Review Day | - | - | - | [prismia] | [zoom] |
| 30 | 11-30 | Markov Chain Monte Carlo | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 31 | 12-02 | Causal Inference | [DG] | [itempool] | [nbviewer] | [prismia] | [zoom] |
| 32 | 12-04 | Final Review | - | [prismia] | - | [prismia] | [zoom] |