Dse 2023-24 — различия между версиями
(added github link) |
Pshuanar (обсуждение | вклад) (→Log Book or Tentative Plan) |
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==Log Book or Tentative Plan == | ==Log Book or Tentative Plan == | ||
| − | [https://www.youtube.com/playlist?list=PLyjahhN4Wdd9rpesSCbxg5YdtF_cJ1SZC | + | [https://www.youtube.com/playlist?list=PLyjahhN4Wdd9rpesSCbxg5YdtF_cJ1SZC 🔗Lecture playlist] |
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| + | [https://www.youtube.com/playlist?list=PLyjahhN4Wdd99xaZOCZ99hNcLiM66QR9E 🔗Consultations playlist] | ||
'''Week 1. 2023-09-04''': Entropy, [https://github.com/Shuaynat/DSE-23-24/raw/main/03-lectures/Dse2023-L01.pdf pdf] | '''Week 1. 2023-09-04''': Entropy, [https://github.com/Shuaynat/DSE-23-24/raw/main/03-lectures/Dse2023-L01.pdf pdf] | ||
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Class: group by, reshape and join | Class: group by, reshape and join | ||
| − | '''Week 3.''': Trees | + | '''Week 3.''': Trees, [https://github.com/Shuaynat/DSE-23-24/raw/main/03-lectures/Dse2023-L03.pdf pdf] |
Class: Trees (regression + classification) + tree visualization | Class: Trees (regression + classification) + tree visualization | ||
| − | '''Week 4.''' Random forest | + | More: |
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| + | [http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ tree visualization by r2d3] | ||
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| + | [https://en.wikipedia.org/wiki/Receiver_operating_characteristic accuracy, recall, roc and all of that] | ||
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| + | '''Week 4.''' Random forest [https://github.com/Shuaynat/DSE-23-24/raw/main/03-lectures/Dse2023-L04.pdf pdf] | ||
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| + | More: | ||
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| + | [http://www.r2d3.us/visual-intro-to-machine-learning-part-2/ bias-variance trade-off visualization for trees] | ||
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| + | [https://arxiv.org/pdf/1411.5279.pdf Tim Hesterberg, What teachers should know about bootstrap?] Very well written text, for permutation test see sections 2.1 and 7. | ||
Class: Random forest, cross-validation in sklearn, feature importance, | Class: Random forest, cross-validation in sklearn, feature importance, | ||
| − | '''Week 5.''': Gradient boosting | + | '''Week 5.''': Gradient boosting, Data splitting strategies |
Class: XGBoost vs LightGBM, Dummy variables, categorical variables and Catboost | Class: XGBoost vs LightGBM, Dummy variables, categorical variables and Catboost | ||
Текущая версия на 16:12, 31 октября 2023
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
Lectures: Monday, 18:10 - 19:30 Moscow time, zoom
Classes:
- Thursday, 13:00 - 14:20 Moscow time, D208, Shuana
Github repository of the class: [1]
Log Book or Tentative Plan
Week 1. 2023-09-04: Entropy, pdf
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, pdf
How to calculate expected values using cross-entropy ideas, H(X) - H(X|Q) as long term interest rate.
More:
Class: group by, reshape and join
Week 3.: Trees, pdf
Class: Trees (regression + classification) + tree visualization
More:
accuracy, recall, roc and all of that
Week 4. Random forest pdf
More:
bias-variance trade-off visualization for trees
Tim Hesterberg, What teachers should know about bootstrap? Very well written text, for permutation test see sections 2.1 and 7.
Class: Random forest, cross-validation in sklearn, feature importance,
Week 5.: Gradient boosting, Data splitting strategies
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!
