Repository for the course Statistical Learning Methods [223491-D] - Spring 2023/24
Required software
During the course we'll use Jupyter Notebook with Python. To run the code provided during classes you'll need:
- Python (for Windows user it's easy to install Python through Anaconda)
- Jupyter Notebook or Jupyter Lab
- Git
Contact
Name: Łukasz Kraiński
Email: lkrain@sgh.waw.pl
You can contact me through MS Teams or come to consultation in G-115, Mondays 08:00-09:40 (with previous alignment on Teams or e-mail).
Lecturers
- lecturer: Bogumił Kamiński
- laboratories: Łukasz Kraiński
Schedule
-
lectures: Mondays, 11:40-13:20
-
laboratories: every second Thursday 9:50-11:30, 11:40-13:20 (check USOS schedule for your group for details)
Lectures
| Date | Subject |
|---|---|
| 2024-02-26 | Introduction |
| 2024-03-04 | Machine learning |
| 2024-03-11 | Linear regression |
| 2024-03-18 | Classification problems |
| 2024-03-25 | Resampling methods |
| 2024-04-08 | Model selection |
| 2024-04-15 | Modeling nonlinear relationships |
| 2024-04-22 | Tree based methods |
| 2024-04-29 | Support vector machines |
| 2024-05-06 | Deep learning |
| 2024-05-13 | Survival analysis |
| 2024-05-20 | Unsupervised learning |
| 2024-05-27 | Multiple testing |
| 2024-06-03 | Course summary; final exam |
Laboratories
| # | Subject |
|---|---|
| 1 | Organizational class; Introduction to Jupyter Notebook and Machine Learning in Python |
| 2 | Methods of evaluation of classifiers |
| 3 | Regularization and cross-validation |
| 4 | Tree-based models (CART, Random Forest, Boosted Trees) |
| 5 | Modeling competition |
| 6 | Practical exam |
| 7 | Non-mandatory consultation |
Literature
- Primary literature
- Materials shared on lectures
- Gareth J., Witten D., Hastie T., Tibshirani R. (2021), An Introduction to Statistical Learning
- Additional resources
- Hastie T., Tibshirani R., Friedman J. (2017), The Elements of Statistical Learning
- Kamiński B. (2022), Julia for Data Analysis
- Mykel J. Kochenderfer, Tim A. Wheeler, And Kyle H. Wray (2022), Algorithms for Decision Making
- Stephen Boyd and Lieven Vandenberghe, Introduction to Applied Linear Algebra
- VanderPlas J. (2016), Python Data Science Handbook
- Géron A. (2019), Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Course evaluation criteria
- Theoretical examination on last lecture (50 points)
- Practical examination on last laboratory (50 points)
- Extra points (send to lkrain@sgh.waw.pl):
- Homeworks
- Laboratory competition
Grading rules
| From | To | Final grade |
|---|---|---|
| 0 | 49 | 2.0 |
| 50 | 59 | 3.0 |
| 60 | 69 | 3.5 |
| 70 | 79 | 4.0 |
| 80 | 89 | 4.5 |
| 90 | 100 | 5.0 |