Dse 2023-24 — различия между версиями
Bdemeshev (обсуждение | вклад) (→Log Book or Tentative Plan) |
Bdemeshev (обсуждение | вклад) (→Log Book or Tentative Plan) |
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| Строка 16: | Строка 16: | ||
| − | '''Week 1. 2023-09-04''' Entropy | + | '''Week 1. 2023-09-04''': Entropy |
Guessing game, conditional entropy, joint entropy. | Guessing game, conditional entropy, joint entropy. | ||
| Строка 26: | Строка 26: | ||
(rus) https://exuberant-arthropod-be8.notion.site/1-02-09-5e107ea1c4054594b8f37d955db8a2b0 | (rus) https://exuberant-arthropod-be8.notion.site/1-02-09-5e107ea1c4054594b8f37d955db8a2b0 | ||
| − | Week 2. Kelly criterion | + | '''Week 2.''': Kelly criterion |
Class: group by, reshape and join | Class: group by, reshape and join | ||
| − | Week 3. Trees | + | '''Week 3.''': Trees |
Class: Trees (regression + classification) + tree visualization | Class: Trees (regression + classification) + tree visualization | ||
| − | Week 4. Random forest + Data splitting strategies | + | '''Week 4.''' Random forest + Data splitting strategies |
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 |
Class: XGBoost vs LightGBM, Dummy variables, categorical variables and Catboost | Class: XGBoost vs LightGBM, Dummy variables, categorical variables and Catboost | ||
| − | Week 6. Naive bootstrap, t-stat bootstrap, permutation tests | + | '''Week 6.''': Naive bootstrap, t-stat bootstrap, permutation tests |
Class: Hypothesis testing | Class: Hypothesis testing | ||
| Строка 50: | Строка 50: | ||
https://arch.readthedocs.io/en/latest/bootstrap/bootstrap.html | https://arch.readthedocs.io/en/latest/bootstrap/bootstrap.html | ||
| − | Week 7. Matrices in regression | + | '''Week 7.''': Matrices in regression |
Class: (by hand) Differential in matrix form, derivation of formulas for beta. | Class: (by hand) Differential in matrix form, derivation of formulas for beta. | ||
| − | Here will be <del>dragons</del> midterm! | + | '''Here will be <del>dragons</del> midterm!''' |
| − | Week 8. SVD = PCA | + | '''Week 8.''': SVD = PCA |
Class: (by hand) Covariance matrices, | Class: (by hand) Covariance matrices, | ||
| − | Week 9. James Stein paradox | + | '''Week 9.''': James Stein paradox |
Class: Matrices in numpy, PCA in sklearn, SVD | Class: Matrices in numpy, PCA in sklearn, SVD | ||
| − | Week 10. L1, L2 regularization | + | '''Week 10.''': L1, L2 regularization |
Class: Regression in sklearn, different type of regularisation | Class: Regression in sklearn, different type of regularisation | ||
| − | Week 11. Log regression + L1/L2 | + | '''Week 11.''': Log regression + L1/L2 |
Class: Log regression (sklearn/statsmodels) + L1/L2 | Class: Log regression (sklearn/statsmodels) + L1/L2 | ||
| − | Week 12. Hierarchical clustering + k-means | + | '''Week 12.''': Hierarchical clustering + k-means |
Class: Hierarchical clustering + k-means | Class: Hierarchical clustering + k-means | ||
| − | Week 13. ETS (Exponential Smoothing) | + | '''Week 13.''': ETS (Exponential Smoothing) |
Class: Plotting time series, ETS (sktime) | Class: Plotting time series, ETS (sktime) | ||
| Строка 86: | Строка 86: | ||
https://www.sktime.net/en/stable/examples/01_forecasting.html | https://www.sktime.net/en/stable/examples/01_forecasting.html | ||
| − | Week 14. Bayesian approach | + | '''Week 14.''': Bayesian approach |
Class: TS forecasting with grad boosting | Class: TS forecasting with grad boosting | ||
| − | Week 15. Mention of MCMC + DLT | + | '''Week 15.''': Mention of MCMC + DLT |
Class: DLT in python | Class: DLT in python | ||
| Строка 100: | Строка 100: | ||
https://www.uber.com/blog/orbit/ | https://www.uber.com/blog/orbit/ | ||
| − | Week 16.QA | + | '''Week 16.''': QA |
Class: QA | Class: QA | ||
Here will be <del>dragons</del> final! | Here will be <del>dragons</del> final! | ||
Версия 16:01, 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!
