Dse 2023-24 — различия между версиями
Bdemeshev (обсуждение | вклад) (Новая страница: «== General course info == 16 lectures plus 16 classes * Boring official web page Fall grade = 0.2 Small HAs + 0.2 Group project + 0.3 Midterm + 0.3 Final Each…») |
Bdemeshev (обсуждение | вклад) (→Log Book or Tentative Plan) |
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Class: (by hand) Differential in matrix form, derivation of formulas for beta. | Class: (by hand) Differential in matrix form, derivation of formulas for beta. | ||
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Версия 15:59, 4 сентября 2023
General course info
16 lectures plus 16 classes
- Boring official web page
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.
Lecturer: Boris Demeshev
Class teachers: Yana Khassan, Shuana Pirbudagova
Log Book or Tentative Plan
Week 1. 2023-09-04 Entropy
Guessing game, conditional entropy, joint entropy.
Class: data manipulation, data vizualization
More:
(rus) 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!