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]
(last year's content)