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

Lecture Recordings

Date	Topic	Lecture materials	Reading
30 jan	1.Introduction	Slides	[FML] Ch 1; [ESL] Ch 2.1-2
6 feb	2.Gradient Optimization	Slides	[FML] Appx A, B; Convex Optimization book
13 feb	3.Linear Regression	Slides, Notebook	[PR] Ch 3.1; [ESL] Ch 3.1-4; [FML] Ch 4.4-6
20 feb	4.Linear Classification	Slides (GLM), Notes (GLM) , Slides (linclass)	[PR] Ch 4.1; [ESL] Ch 4.1-2, 4.4; [FML] Ch 13
27 feb	5.Logistic Regression and SVM	Slides	[ESL] Ch 12.1-3; [FML] Ch 5, 6
6 mar	6.Decision Trees	Slides	[ESL] Ch 9.2
12 mar	7.Bagging, Random Forest	Slides, Notebook	[PR] Ch 3.2 (bias-variance) [ESL] Ch 8 [FML] Ch 7
19 mar	8.Gradient boosting	Slides	[PR] Ch 14.3 [ESL] Ch 10
22 mar - 4 apr	NO LECTURES	---	---
9 apr	9.Clustering and Anomaly Detection	Slides, Notebook	[PR] Ch 9.1; [ESL] Ch 13.2, 14.3
16 apr	10.EM and PCA	Lecture notes	[PR] Ch 9.2-9.4; [ESL] Ch 8.5
23 apr	11.Bayesian Linear Regression	Slides	[PR] Ch 2.3-2.4, 3.3-3.5
30 apr	12.GP for regression and classification tasks		[PR] Ch 6.4
14 may	13.MLP and DNN for Classification	Slides	[PR] Ch 5.1-5.5; [ESL] Ch 11
21 may	14.Deep Generative Models		Overview
28 may	15.Summary

Practicals

Date	Topic	Materials	Extra Reading/Practice
25-30 jan	1.Basic toolbox	Notebook; Dataset	Python Crash Course
1-6 feb	2.EDA and Scikit-learn	Notebook
8-13 feb	3.Calculus recap and Gradient Descent	Notebook, pdf	The Matrix Cookbook
15-20 feb	4.Linear Regression	Notebook
22-27 feb	5.Classification	Notebook
1-6 mar	6.Texts and Multiclass classification	Notebook, Dataset
8-13 mar	7.Decision Trees	Notebook
15-20 mar	8.Ensembles	Notebook
5-10 apr	9.Gradient Boosting	Notebook
12-17 apr	10.Anomaly detection and Clustering	Notebook
19-24 apr	11.EM	Tasks
25-30 apr	12.Empirical Bayes and RVM	Notebook	[PR] Ch 7.2
10-15 may	13.GP	Notebook
17-22 may	14.MLP	Notebook
24-29 may	15.Summary	Slides

Assignments

We'll be using AnyTask for grading: course link

Date Published	Task	Deadline
6 feb	HW 1: Notebook, dataset	20 feb
26 feb	HW 2: Notebook	13 mar
14 mar	HW 3: Notebook	4 apr
10 apr	HW 4: Notebook, dataset	1 may
3 may	HW 5: Notebook, datasets	24 may
31 may	HW 6 Task: pdf, Task: tex	10 june

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