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	<title>Wiki - Факультет компьютерных наук - Вклад [ru]</title>
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	<updated>2026-09-21T11:26:59Z</updated>
	<subtitle>Вклад</subtitle>
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	<entry>
		<id>https://wiki.cs.hse.ru/index.php?title=Dse_2023-24&amp;diff=79777</id>
		<title>Dse 2023-24</title>
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		<updated>2023-09-14T16:39:33Z</updated>

		<summary type="html">&lt;p&gt;Yanikamarvel5: added github link&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== General course info ==&lt;br /&gt;
&lt;br /&gt;
16 lectures plus 16 classes&lt;br /&gt;
&lt;br /&gt;
Fall grade = 0.2 Small HAs + 0.2 Group project + 0.3 Midterm + 0.3 Final&lt;br /&gt;
&lt;br /&gt;
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. &lt;br /&gt;
&lt;br /&gt;
Lecturer: Boris Demeshev&lt;br /&gt;
&lt;br /&gt;
Class teachers: [https://www.hse.ru/org/persons/190922066 Yana Khassan], Shuana Pirbudagova&lt;br /&gt;
&lt;br /&gt;
[https://t.me/+wieDS2ZjAvVjYTJi tg group], &lt;br /&gt;
&lt;br /&gt;
Lectures: Monday, 18:10 - 19:30 Moscow time, [https://zoom.us/j/8126338383 zoom]&lt;br /&gt;
&lt;br /&gt;
Classes: &lt;br /&gt;
* Thursday, 13:00 - 14:20 Moscow time, D208, Shuana&lt;br /&gt;
&lt;br /&gt;
Github repository of the class: [https://github.com/Shuaynat/DSE-23-24/tree/main]&lt;br /&gt;
&lt;br /&gt;
==Log Book or Tentative Plan ==&lt;br /&gt;
&lt;br /&gt;
[https://www.youtube.com/playlist?list=PLyjahhN4Wdd9rpesSCbxg5YdtF_cJ1SZC Lecture playlist]&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 1. 2023-09-04&#039;&#039;&#039;: Entropy, [https://github.com/Shuaynat/DSE-23-24/raw/main/03-lectures/Dse2023-L01.pdf pdf]&lt;br /&gt;
&lt;br /&gt;
Guessing game, conditional entropy, joint entropy.&lt;br /&gt;
&lt;br /&gt;
Class: data manipulation, data vizualization&lt;br /&gt;
&lt;br /&gt;
More:&lt;br /&gt;
&lt;br /&gt;
Cristopher Olah, Visual Information Theory &lt;br /&gt;
https://colah.github.io/posts/2015-09-Visual-Information/&lt;br /&gt;
&lt;br /&gt;
Grand Sanderson, Solving Wordle using information theory&lt;br /&gt;
https://www.youtube.com/watch?v=v68zYyaEmEA&lt;br /&gt;
&lt;br /&gt;
конспект аналогичной лекции на фкн:&lt;br /&gt;
https://exuberant-arthropod-be8.notion.site/1-02-09-5e107ea1c4054594b8f37d955db8a2b0&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 2.&#039;&#039;&#039;: Kelly criterion, [https://github.com/Shuaynat/DSE-23-24/raw/main/03-lectures/Dse2023-L02.pdf pdf]&lt;br /&gt;
&lt;br /&gt;
How to calculate expected values using cross-entropy ideas, H(X) - H(X|Q) as long term interest rate. &lt;br /&gt;
&lt;br /&gt;
More:&lt;br /&gt;
&lt;br /&gt;
[https://en.wikipedia.org/wiki/Kelly_criterion Kelly criterion]&lt;br /&gt;
&lt;br /&gt;
Class: group by, reshape and join&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 3.&#039;&#039;&#039;: Trees&lt;br /&gt;
&lt;br /&gt;
Class: Trees (regression + classification) + tree visualization&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 4.&#039;&#039;&#039; Random forest + Data splitting strategies&lt;br /&gt;
&lt;br /&gt;
Class: Random forest, cross-validation in sklearn, feature importance, &lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 5.&#039;&#039;&#039;: Gradient boosting&lt;br /&gt;
&lt;br /&gt;
Class: XGBoost vs LightGBM, Dummy variables, categorical variables and Catboost &lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 6.&#039;&#039;&#039;: Naive bootstrap, t-stat bootstrap, permutation tests&lt;br /&gt;
&lt;br /&gt;
Class: Hypothesis testing &lt;br /&gt;
&lt;br /&gt;
More:&lt;br /&gt;
&lt;br /&gt;
https://arch.readthedocs.io/en/latest/bootstrap/bootstrap.html&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 7.&#039;&#039;&#039;: Matrices in regression&lt;br /&gt;
&lt;br /&gt;
Class: (by hand) Differential in matrix form, derivation of formulas for beta.&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Here will be &amp;lt;del&amp;gt;dragons&amp;lt;/del&amp;gt; midterm!&#039;&#039;&#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 8.&#039;&#039;&#039;: SVD = PCA&lt;br /&gt;
&lt;br /&gt;
Class: (by hand) Covariance matrices, &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 9.&#039;&#039;&#039;: James Stein paradox&lt;br /&gt;
&lt;br /&gt;
Class: Matrices in numpy, PCA in sklearn, SVD&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 10.&#039;&#039;&#039;: L1, L2 regularization&lt;br /&gt;
&lt;br /&gt;
Class: Regression in sklearn, different type of regularisation&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 11.&#039;&#039;&#039;: Log regression + L1/L2&lt;br /&gt;
&lt;br /&gt;
Class: Log regression (sklearn/statsmodels) + L1/L2&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 12.&#039;&#039;&#039;: Hierarchical clustering + k-means&lt;br /&gt;
&lt;br /&gt;
Class: Hierarchical clustering + k-means&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 13.&#039;&#039;&#039;: ETS (Exponential Smoothing)&lt;br /&gt;
&lt;br /&gt;
Class: Plotting time series, ETS (sktime)&lt;br /&gt;
&lt;br /&gt;
More: &lt;br /&gt;
&lt;br /&gt;
https://www.sktime.net/en/stable/examples/01_forecasting.html&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 14.&#039;&#039;&#039;: Bayesian approach&lt;br /&gt;
&lt;br /&gt;
Class: TS forecasting with grad boosting &lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 15.&#039;&#039;&#039;: Mention of MCMC + DLT&lt;br /&gt;
&lt;br /&gt;
Class: DLT in python&lt;br /&gt;
&lt;br /&gt;
More: &lt;br /&gt;
&lt;br /&gt;
Mcmc visualization: https://chi-feng.github.io/mcmc-demo/app.html?algorithm=SVGD&amp;amp;target=banana&amp;amp;delay=0 &lt;br /&gt;
&lt;br /&gt;
https://www.uber.com/blog/orbit/&lt;br /&gt;
&lt;br /&gt;
&#039;&#039;&#039;Week 16.&#039;&#039;&#039;: QA &lt;br /&gt;
&lt;br /&gt;
Class: QA&lt;br /&gt;
&lt;br /&gt;
Here will be &amp;lt;del&amp;gt;dragons&amp;lt;/del&amp;gt; final!&lt;/div&gt;</summary>
		<author><name>Yanikamarvel5</name></author>
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