Просмотр исходного текста страницы Icef-dse-2024-fall

== General course info ==

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

We expect 3 practice HA and 3 theory HA. 

Lecturer: Boris Demeshev

Class teachers: [https://www.hse.ru/org/persons/190922066 Yana Khassan], [https://www.hse.ru/org/persons/190908793 Shuana Pirbudagova]

Lecture [https://e.pcloud.link/publink/show?code=kZmgWPZHzY4Nt5X8ypIIooweB0uqpNzDpIV video recordings]

Telegram [https://t.me/+HOr0rTvMTAs3MWUy group]

==Log Book or Tentative Plan ==

2024-09-05, lecture 1: Entropy, conditional entropy, joint entropy, mutual information, cross-entropy. 

* 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 youtube].

* Artem Kirsanov, Key equation behind probability, [https://www.youtube.com/watch?v=KHVR587oW8I youtube]. Be careful, Artem uses notation H(P, Q) for Cross entropy (we use CE(P||Q)).

* [https://exuberant-arthropod-be8.notion.site/1-02-09-5e107ea1c4054594b8f37d955db8a2b0 Конспект] аналогичной лекции на фкн на русском.

* Keith Conrad, [https://kconrad.math.uconn.edu/blurbs/analysis/entropypost.pdf Maximal entropy] distributions.

2024-09-12, lecture 2: Expected value of log-likelihood is zero. Kullback-Leibler divergence definition. Expected value calculation example. Optimizing long-run profit. Horse betting: optimal bet under private signal. 

* Marcin Anforowicz, Just one more paradox [https://www.youtube.com/watch?v=_FuuYSM7yOo youtube]

* Wikipedia, [Kelly Criterion https://en.wikipedia.org/wiki/Kelly_criterion]: a good article

* Kelly, [https://www.princeton.edu/~wbialek/rome/refs/kelly_56.pdf A new interpretation of information rate]: original paper, very well written

2024-09-19, lecture 3: Horse betting: optimal bet under signal. Optimal long-term interest rate as entropy difference. How to build a tree? Entropy drop as splitting criterion. Dealing with missing values. 
How to stop? Tree pruning.

* R2D3, Visual introduction to machine learning: [http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ decision tree]

2024-09-26, lecture 4: Random forest

* R2D3, Visual introduction to machine learning-2: [http://www.r2d3.us/visual-intro-to-machine-learning-part-2/ bias-variance tradeoff and many trees]

2024-10-03, lecture 5: Bootstrap: Naive bootstrap, t-stat bootstrap, bootstrap in bootstrap. 

* Tim Hesterberg, [https://arxiv.org/pdf/1411.5279 What teachers should know about bootstrap?]

2024-10-10, lecture 6: Gradient boosting for regression. Residual vector as minus gradient. Properties of logistic function.

* Alexey Natekin, Alois Knoll, [https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2013.00021/full Gradient boosting] machines.

* Cheng Li, Gentle Introduction to [https://www.chengli.io/tutorials/gradient_boosting.pdf Gradient Boosting]

2024-10-17, lecture 7: Gradient of logit model in general form. One-to-one correspondence between probabilities and log-odds. Gradient boosting for classification.

2024-10-24, lecture 8: Cross validation: leave-one-out, k-fold. Importance for random forest: mean decrease of impurity. Permutation based importance.

2024-11-07, lecture 9: Differential in a matrix form, derivation of beta hat in multivariate regression.

2024-11-07: Midterm

2024-11-14, lecture 10: Variances and covariance in multivariate regression using matrices

2024-11-21, lecture 11: SVD, PCA as average R2 optimization



==Past courses==

Fall 2023: [http://wiki.cs.hse.ru/Dse_2023-24 wiki page], [https://github.com/Shuaynat/DSE-23-24/tree/main/00-exams exams]. 

Fall 2022: [http://wiki.cs.hse.ru/Icef-dse-2022-23 wiki].