Contacts

Telegram chat

Lecturers: Anna Kuzina; Evgenii Egorov

Class Teachers and TAs

Class Teachers	Contact	Group	TA (contact)
Maria Tikhonova	tg: @mashkka_t	БПИ184	Alexandra Kogan (tg: @horror_in_black)
Maksim Karpov	tg: @buntar29	БПИ181, БПИ182	Kirill Bykov (tg: @darkydash), Victor Grishanin (tg: @vgrishanin)
Polina Polinuna	tg: @ppolunina	БПИ185	Michail Kim (tg: @kimihailv)
Vadim Kokhtev	tg: @despairazure	БПИ183	Daniil Kosakin (tg: @nieto95)

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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
30 jan	Introduction	Slides, Video	[FML] Ch 1; [ESL] Ch 2.1-2.2
6 feb	Gradient Optimization		[FML] Appx A, B;
13 feb	Linear Regression
20 feb	Linear Classification
27 feb	Logistic Regression and SVM
6 mar	Decision Trees
13 mar	Bagging, Random Forest
20 mar	Gradient boosting

Practicals

Date	Topic	Materials	Extra Reading/Practice
25-30 jan	Basic toolbox	Notebook; Dataset	Python Crash Course
1-6 feb	EDA and Scikit-learn	Notebook
8-13 feb	Calculus recap and Gradient Descent		The Matrix Cookbook
15-20 feb	Linear Regression
22-27 feb	Classification
1-6 mar	Texts and Multiclass classification
8-13 mar	Decision Trees
15-20 mar	Ensambles

Assignments

We'll be using AnyTask for grading: course link

Date Published	Task	Deadline
6 feb	HW 1: Notebook, dataset	20 feb
20 feb	HW 2: TBA	6 mar
6 mar	HW 3: TBA	20 mar
20 mar	HW 4: TBA	10 apr
24 apr	HW 5: TBA	15 may
29 may	HW 6 (Optional): TBA	19 jun

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.
• Exam - Grade for the exam

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You can skip the exam if your average grade for the first 5 assignemnts is not smaller than 6 (HW >=6). In this case:

Final grade = HW