Dse 2023-24

General course info

16 lectures plus 16 classes

Fall grade = 0.2 Small HAs + 0.2 Group project + 0.3 Midterm + 0.3 Final

Each small HA consists of approximately 4 or 5 problems. Group project may be written by a group of 1-3 students. Midterm and Final are offline and hand written.

Lecturer: Boris Demeshev

Class teachers: Yana Khassan, Shuana Pirbudagova

tg group,

Lectures: Monday, 18:10 - 19:30 Moscow time, zoom

Log Book or Tentative Plan

Week 1. 2023-09-04: Entropy

Guessing game, conditional entropy, joint entropy.

Class: data manipulation, data vizualization

More:

Cristopher Olah, Visual Information Theory https://colah.github.io/posts/2015-09-Visual-Information/

Grand Sanderson, Solving Wordle using information theory https://www.youtube.com/watch?v=v68zYyaEmEA

конспект аналогичной лекции на фкн: https://exuberant-arthropod-be8.notion.site/1-02-09-5e107ea1c4054594b8f37d955db8a2b0

Week 2.: Kelly criterion

Class: group by, reshape and join

Week 3.: Trees

Class: Trees (regression + classification) + tree visualization

Week 4. Random forest + Data splitting strategies

Class: Random forest, cross-validation in sklearn, feature importance,

Week 5.: Gradient boosting

Class: XGBoost vs LightGBM, Dummy variables, categorical variables and Catboost

Week 6.: Naive bootstrap, t-stat bootstrap, permutation tests

Class: Hypothesis testing

More:

https://arch.readthedocs.io/en/latest/bootstrap/bootstrap.html

Week 7.: Matrices in regression

Class: (by hand) Differential in matrix form, derivation of formulas for beta.

Here will be dragons midterm!

Week 8.: SVD = PCA

Class: (by hand) Covariance matrices,

Week 9.: James Stein paradox

Class: Matrices in numpy, PCA in sklearn, SVD

Week 10.: L1, L2 regularization

Class: Regression in sklearn, different type of regularisation

Week 11.: Log regression + L1/L2

Class: Log regression (sklearn/statsmodels) + L1/L2

Week 12.: Hierarchical clustering + k-means

Class: Hierarchical clustering + k-means

Week 13.: ETS (Exponential Smoothing)

Class: Plotting time series, ETS (sktime)

More:

https://www.sktime.net/en/stable/examples/01_forecasting.html

Week 14.: Bayesian approach

Class: TS forecasting with grad boosting

Week 15.: Mention of MCMC + DLT

Class: DLT in python

More:

Mcmc visualization: https://chi-feng.github.io/mcmc-demo/app.html?algorithm=SVGD&target=banana&delay=0

https://www.uber.com/blog/orbit/

Week 16.: QA

Class: QA

Here will be dragons final!