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Repository Details

Open Machine Learning course

Machine Learning course

First semester of girafe-ai Machine Learning course

Recordings and materials

Date Content Lecture video Slides WarmUp test HW Deadline Comments
05.09.2022 Week01. Intro, Naive Bayes and kNN. Запись лекции 2021 Запись семинара 2021 Слайды Assignment 01: kNN 23.59 AOE, 03.10.2022 По техническим причинам запись лекции 2022 года не велась
12.09.2022 extra Week. Linear algebra recap. Запись лекции Запись семинара 2022 Слайды
19.09.2022 Week02. Linear Regression. Запись лекции Запись семинара 2022 Слайды Assignment 02: Linear Regression 23.59 AOE, 10.10.2022
26.09.2022 Week03. Linear Classification. Запись лекции Запись семинара 2022 Слайды Lab01: ML pipeline 23.59 AOE 10.11.2022
03.10.2022 Week04. SVM, PCA. Запись лекции Запись семинара 2022 Слайды Assignment 03: SVM kernel 23.59 AOE, 24.10.2022
10.10.2022 Week05. Trees and ensembles Запись лекции Слайды Optional assignment 04: Tree from scratch 23.59 AOE, 22.12.2022 Вместо семинара проходила контрольная работа
17.10.2022 Week06. Gradient boosting Запись лекции Запись семинара Слайды
24.10.2022 Week07. Аnalysis of the testing Запись разбора Вместо лекции были тест и разбор.
31.10.2022 Week08. Intro into Deep Learning Запись лекции Запись семинара Слайды
07.11.2022 Week09. Backpropogation Запись семинара Слайды Лекция не велась по причине болезни преподавателя, однако был проведён дополнительный семинар по backpropogation
14.11.2022 Week10. Dropout and Batchnorm Запись лекции Запись семинара Слайды
21.11.2022 Week11. Embeddings and seq2seq model Запись лекции Запись семинара Слайды

Prerequisites

Prerequisites are located here.

Literature:

  1. YSDA ML Book (Russian only)
  2. Probabilistic Machine Learning: An Introduction; English link, Русский перевод
  3. Deep Learning Book: English link. Первая часть (Part I) крайне рекомендуется к прочтению.

More additional materials are available here

Exam program:

Available here

Main authors:

  • Radoslav Neychev
  • Vladislav Goncharenko

Contributors:

  • Iurii Efimov
  • Nikolay Karpachev
  • Ivan Provilkov
  • Valery Marchenkov
  • Anastasia Ianina
  • Irina Rudenko
  • Fedor Ryabov

Acknowledgements:

Special thanks to:

  • Stanislav Fedotov, YSDA for informative discussions, program verification and support.
  • Konstantiv Vorontsov
  • Vadim Strijov for teaching this course teachers
  • Just Heuristic

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