This course makes introduction to Machine Learning (ML) field. ML is a branch of artificial intelligence (AI) that enables computers to learn from data and make decisions or predictions without being explicitly programmed. ML is used in applications like speech recognition, internet search engines, recommendation systems, and so on. The course presents different methods for solving supervised regression and classification problems like linear and logistic regression, support vector machines and decision trees. Then the course discusses composition of ML algorithms, the problem of bias-variance decomposition and the methods like random forest and gradient boosting. The course also covers topics of relevant feature selection, unsupervised ML problems and ranking. Together with ML-related material the course includes necessary discussion of basic mathematical tools actively used in ML like probability and statistics, optimization algorithms and vector-matrix calculus.
Instructor: Dmitry Kropotov
Timetable: the classes are scheduled on Fridays 8:15 - 11:00 in SAC, Hall 3. The first lecture is scheduled on the 6th of February.
Chat for questions: please ask your questions in the corresponding Teams space
Assignments: all assignments are given and checked in the corresponding Teams space
Video recordings: in the course materials relevant video recordings are provided. However, please note that these videos are somewhat outdated and do not cover all course topics. Please consider the presentations as the main source for topics covered in the classes.
Written examination, Duration: 120 min, Weight: 100 %
Completion: To pass this module, the exam must be passed with at least 45%.
In the course there will be given several home assignments in the form of Jupyter notebooks and theoretical assignments on mathematical topics like matrix calculus, optimization, etc. Completing these assignments is fully optional. However, there will be a small bonus for making these assignments: 5% to the final course grade in case of total assignments grade between 30% and 65%, and 10% to the final course grade in case of total assignments grade higher than 65%.
In the middle of the course a mid-term written exam is planned. This exam helps students better understand the types of problems that are expected in the final course exam. For successfull passing of this mid-term exam a small bonus is supposed: 5% to the final course grade in case of mid-term grade between 45% and 65%, and 10% to the final course grade in case of mid-term grade higher than 65%.
The described bonuses can't exceed together 10%.
The final written exam will be offline on campus. Test exams from previous years:
Mid-term exam test variant: exam, reference
Final exam test variant: exam, reference
Mid-term exam 2026 additional tasks: tasks, reference
| Date | Number | Topic | Materials |
|---|---|---|---|
| 06.02.26 | 01 | Introduction to the course. Basic terminology in ML, feature types, standard ML problem types. Overfitting and cross-validation. Pandas library and exploratory data analysis. | Presentation ipynb Videos: 1, 2, 3, 4 |
| 13.02.26 | 02 | Linear regression. Loss functions for regression, L2/L1/ElasticNet regularization. Matrix/vector differentiation. | Presentation Videos: 1, 2, 3, 4, 5, 6, 7 |
| 20.02.26 | 03 | Linear regression: optimization and data normalization. | Presentation Videos: 1, 2, 3, 4, 5, 6 |
| 27.02.26 | 04 | Linear regression: probabilistic view. Linear classification | Presentation Videos: 1, 2, 3, 4 |
| 06.03.26 | 05 | Constrained optimization. Support Vector Machine (SVM). Feature transformations | Presentation Videos: 1, 2, 3, 4, 5 |
| 13.03.26 | 06 | Logistic Regression. Probability calibration. Multiclass Classification. Hyperparameter optimization. | Presentation Videos: 1, 2, 3, 4 |
| 20.03.26 | 07 | Decision Trees for regression and classification. Feature importance. | Presentation Videos: 1, 2, 3, 4 |
| 27.03.26 | 08 | Mid-term exam. | |
| 03.04.26 | -- | Semester break. No classes. | |
| 10.04.26 | 09 | Bias-Variance Decomposition. Random Forest and Gradient Boosting. | Presentation Videos: 1, 2, 3, 4, 5, 6 |
| 17.04.26 | 10 | Gradient Boosting implementations: XGBoost, LightGBM, CatBoost. Blending and Stacking. | Presentation Videos: 1, 2, 3 |
| 24.04.26 | -- | No classes | |
| 01.05.26 | -- | Public holiday. No classes. | |
| 08.05.26 | 11 | Unsupervised learning: clustering, dimension reduction, data visualization | Presentation Videos: 1, 2, 3, 4 |
| 15.05.26 | 12 | Learning to Rank | Presentation Videos: 1, 2, 3, 4 |
- T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, Springer, 2008.
- S. Shalev-Shwartz, Shai Ben-David: Understanding Machine Learning, Cambridge University Press, 2014.
- C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
- T.M. Mitchell, Machine Learning, Mc Graw Hill India, 2017.