girafe-ai/ml-course
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Open Machine Learning course
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README.md
OriginalСсылка на ветку ML тренировок Яндекса 2023
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. Разбор теста | 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:
- YSDA ML Book (Russian only)
- Probabilistic Machine Learning: An Introduction; English link, Русский перевод
- 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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