Contacts
Lecturers: Polina Polunina, Semeon Budennyy
Class Teachers and TAs
| Class Teachers | Group | TA (contact) |
|---|---|---|
| Andrei Egorov | БПИ201, БПИ202 | Andrei Dyadynov (tg: @mr_dyadyunov), Nikita Tatarinov (tg: @NickyOL) |
| Kirill Bykov | БПИ203, БПИ204 | Anastasia Egorova (tg: @wwhatisitt), Elizaveta Berdina (tg: @berdina_elis) |
| Maria Tikhonova | БПИ205 | Alexander Stepin (tg: @kevicia) |
| Anastasia Voronkova | БПИ206, БПИ207 | Anton Alekseev (tg: @flameglamebeatskilla), Emil Akopyan (tg: @archivarius) |
Recomended Literature
[PR] Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg.
Link
[ESL] Hastie, T., Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
Link
[FML] Mohri, M., Talwalkar, A., & Rostamizadeh, A. Second Edition, (2018). Foundations of Machine Learning. Cambridge, MA: The MIT Press.
Link
Class materials
Lectures
| Date | Topic | Lecture materials | Reading |
|---|---|---|---|
| 5 sep | 1.Introduction | [FML] Ch 1; [ESL] Ch 2.1-2 | |
| 12 sep | 2.Gradient Optimization | [FML] Appx A, B; Convex Optimization book | |
| 19 sep | 3.Linear Regression | [PR] Ch 3.1; [ESL] Ch 3.1-4; [FML] Ch 4.4-6 | |
| 26 sep | 4.Linear Classification | [PR] Ch 4.1; [ESL] Ch 4.1-2, 4.4; [FML] Ch 13 | |
| 3 oct | 5.Logistic Regression and SVM | [ESL] Ch 12.1-3; [FML] Ch 5, 6 | |
| 10 oct | 6.Decision Trees | [ESL] Ch 9.2 | |
| 17 oct | 7.Bagging, Random Forest | [PR] Ch 3.2 (bias-variance); [ESL] Ch 8; [FML] Ch 7 | |
| 24 oct - 30 oct | NO LECTURES | --- | --- |
| 31 oct | 8.Gradient boosting | [PR] Ch 14.3; [ESL] Ch 10 | |
| 7 nov | 9.Clustering and Anomaly Detection | [PR] Ch 9.1; [ESL] Ch 13.2, 14.3 | |
| 14 nov | 10.Dimensionality reduction: PCA, SVD | [ESL] Ch 14.5; [PR] Ch 12.1 | |
| 21 nov | 11.Testing your models: AA/AB tests | ||
| 28 nov | 12.MLP and basic NN | [PR] Ch 5.1-5.5; [ESL] Ch 11 | |
| 5 dec | 13.Basic CV: convolutional layer | ||
| 12 dec | 14.ML: business applications | ||
| 19 dec | 15.Summary |
Practicals
| Date | Topic | Materials | Extra Reading/Practice |
|---|---|---|---|
| 6-10 sep | 1.Basic toolbox | Notebook; Dataset | Python Crash Course |
| 13-17 sep | 2.EDA and Scikit-learn | Notebook | |
| 20-24 sep | 3.Calculus background: Matrix-Vec differention and GD | Notebook; Matrix-vector differentiation | The Matrix Cookbook |
| 27-1 oct | 4.Linear Regression | Notebook | |
| 4-8 oct | 5.Classification metrics | Notebook | |
| 11-15 oct | 6.NLP & SVM | Notebook | NLP For You - great online course |
| 18-22 oct | 7.Decision Trees | Notebook | Guide2DT |
| 1-5 nov | 8.Ensembles | Notebook | |
| 8-12 nov | 9.Gradient Boosting | Notebook | |
| 15-19 nov | 10.Anomaly detection and Clustering | ||
| 22-26 nov | 11.Dimension reduction: PCA, SVD | ||
| 29-3 dec | 12.AA/AB tests | Notebook | |
| 6-10 dec | 13.MLP and basic NN | ||
| 13-17 dec | 14.Basic CV: convolutional layer | ||
| 20-24 dec | 15.Exam preparation, summary |
Grading
Final grade = 0.7*HW + 0.3*Exam
-
HW- Average grade for the assignments 1 to 5. You can get extra points by solving HW 6, but no more than 10 in total. Namely,HW = (hw1 + hw2 + hw3 + hw4 + hw5 + hw6)/5 -
Exam- Grade for the exam.
You can skip the exam if mean grade for the assignemnts are not smaller than 5.5, i.e. (HW >=5.5).
In this case:
Final grade = ROUND(HW)