| 09-09 |
Programming in Julia |
[DG] |
[notebook] |
[video] |
| 09-06 |
Linear Algebra Practice |
[DG] [video] |
[notebook] |
[video] |
| 09-09 |
Eigenvectors and Eigenvalues |
[DG] [video] |
[notebook] |
[video] [video] |
| 09-11 |
Multivariable Calculus Review |
[DG] |
[notebook] |
[video] |
| 09-13 |
Matrix differentiation |
[DG] [video] |
[notebook] |
[video] |
| 09-16 |
Machine arithmetic, numerical error |
[DG] [video] |
[notebook] |
[video] |
| 09-18 |
Pseudorandom numbers, Automatic differentiation |
[DG] [video] |
[notebook] |
[video] |
| 09-20 |
Gradient descent algorithms |
[DG] |
[notebook] |
[video] |
| 09-23 |
Probability Models |
[DG] [video] |
[notebook] |
[video] |
| 09-25 |
Bayes' theorem and conditional expectation |
[DG] [video] |
[notebook] |
[video] |
| 09-27 |
Common distributions and central limit theorem |
[DG] |
[notebook] |
[video] |
| 09-30 |
Simulation techniques and introduction to statistics |
[DG] |
[notebook] |
[video] |
| 10-02 |
Kernel density estimation |
[DG] |
[notebook] |
[video] |
| 10-04 |
Point estimation and confidence intervals |
[DG] |
[notebook] |
[video] |
| 10-07 |
Empirical CDF convergence and bootstrapping |
[DG] |
[notebook] |
[video] |
| 10-09 |
Maximum likelihood estimation and hypothesis testing |
[DG] |
[notebook] |
[video] |
| 10-11 |
Statistical Learning Theory |
[DG] |
[notebook] |
[video] |
| 10-16 |
Linear Regression and Quadratic Discriminant Analysis |
[DG] |
[notebook] |
[video] |
| 10-18 |
Likelihood ratio classification |
[DG] |
[notebook] |
[video] |
| 10-21 |
Generative models (QDA, LDA, Naive Bayes) |
[DG] |
[notebook] |
[video] |
| 10-23 |
Logistic regression |
[DG] |
[notebook] |
[video] |
| 10-25 |
Support Vector Machines (I) |
[DG] |
[notebook] |
[video] |
| 10-28 |
Support Vector Machines (II) |
[DG] |
[notebook] |
[video] |
| 10-30 |
Decision Trees |
[DG] |
[notebook] |
[video] |
| 11-01 |
Ensemble Methods |
[DG] |
[notebook] |
[video] |
| 11-06 |
Neural Networks (I) |
[DG] [3B1B] |
[notebook] |
[video] |
| 11-08 |
Neural Networks (II) |
[DG] [3B1B] |
[notebook] |
[video] |
| 11-11 |
Dimension Reduction |
[DG] [colah] |
[notebook] |
[video] |
| 11-13 |
Bayesian Statistics |
[DG] |
[notebook] |
[video] |
| 11-15 |
Markov Chain Monte Carlo |
[DG] |
[notebook] |
[video] |
| 11-18 |
Bayes nets and Expectation-Maximization |
[DG] |
[notebook] |
[video] |
| 11-22 |
Expectation-Maximization and Hidden Markov Models |
[DG] |
[notebook] |
[video] |
| 11-25 |
Hidden Markov Models: Expectation-Maximization and Probabilistic Programming |
[DG] |
[notebook] |
[video] |
| 12-02 |
Causal Inference |
[DG] |
[notebook] |
[video] |
| 12-04 |
Query packages |
[video] [R4DS] |
[notebook] |
[video] |