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. | ΠΠ°ΠΏΠΈΡΡ Π»Π΅ΠΊΡΠΈΠΈ 2021 ΠΠ°ΠΏΠΈΡΡ ΡΠ΅ΠΌΠΈΠ½Π°ΡΠ° 2021 | Π‘Π»Π°ΠΉΠ΄Ρ | Assignment 01: kNN | 23.59 AOE, 03.10.2022 | ΠΠΎ ΡΠ΅Ρ Π½ΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΏΡΠΈΡΠΈΠ½Π°ΠΌ Π·Π°ΠΏΠΈΡΡ Π»Π΅ΠΊΡΠΈΠΈ 2022 Π³ΠΎΠ΄Π° Π½Π΅ Π²Π΅Π»Π°ΡΡ | |
| 12.09.2022 | extra Week. Linear algebra recap. | ΠΠ°ΠΏΠΈΡΡ Π»Π΅ΠΊΡΠΈΠΈ ΠΠ°ΠΏΠΈΡΡ ΡΠ΅ΠΌΠΈΠ½Π°ΡΠ° 2022 | Π‘Π»Π°ΠΉΠ΄Ρ | ||||
| 19.09.2022 | Week02. Linear Regression. | ΠΠ°ΠΏΠΈΡΡ Π»Π΅ΠΊΡΠΈΠΈ ΠΠ°ΠΏΠΈΡΡ ΡΠ΅ΠΌΠΈΠ½Π°ΡΠ° 2022 | Π‘Π»Π°ΠΉΠ΄Ρ | Assignment 02: Linear Regression | 23.59 AOE, 10.10.2022 | ||
| 26.09.2022 | Week03. Linear Classification. | ΠΠ°ΠΏΠΈΡΡ Π»Π΅ΠΊΡΠΈΠΈ ΠΠ°ΠΏΠΈΡΡ ΡΠ΅ΠΌΠΈΠ½Π°ΡΠ° 2022 | Π‘Π»Π°ΠΉΠ΄Ρ | Lab01: ML pipeline | 23.59 AOE 10.11.2022 | ||
| 03.10.2022 | Week04. SVM, PCA. | ΠΠ°ΠΏΠΈΡΡ Π»Π΅ΠΊΡΠΈΠΈ ΠΠ°ΠΏΠΈΡΡ ΡΠ΅ΠΌΠΈΠ½Π°ΡΠ° 2022 | Π‘Π»Π°ΠΉΠ΄Ρ | Assignment 03: SVM kernel | 23.59 AOE, 24.10.2022 | ||
| 10.10.2022 | Week05. Trees and ensembles | ΠΠ°ΠΏΠΈΡΡ Π»Π΅ΠΊΡΠΈΠΈ | Π‘Π»Π°ΠΉΠ΄Ρ | Optional assignment 04: Tree from scratch | 23.59 AOE, 22.12.2022 | ΠΠΌΠ΅ΡΡΠΎ ΡΠ΅ΠΌΠΈΠ½Π°ΡΠ° ΠΏΡΠΎΡ ΠΎΠ΄ΠΈΠ»Π° ΠΊΠΎΠ½ΡΡΠΎΠ»ΡΠ½Π°Ρ ΡΠ°Π±ΠΎΡΠ° | |
| 17.10.2022 | Week06. Gradient boosting | ΠΠ°ΠΏΠΈΡΡ Π»Π΅ΠΊΡΠΈΠΈ ΠΠ°ΠΏΠΈΡΡ ΡΠ΅ΠΌΠΈΠ½Π°ΡΠ° | Π‘Π»Π°ΠΉΠ΄Ρ | ||||
| 24.10.2022 | Week07. Πnalysis of the testing | ΠΠ°ΠΏΠΈΡΡ ΡΠ°Π·Π±ΠΎΡΠ° | ΠΠΌΠ΅ΡΡΠΎ Π»Π΅ΠΊΡΠΈΠΈ Π±ΡΠ»ΠΈ ΡΠ΅ΡΡ ΠΈ ΡΠ°Π·Π±ΠΎΡ. | ||||
| 31.10.2022 | Week08. Intro into Deep Learning | ΠΠ°ΠΏΠΈΡΡ Π»Π΅ΠΊΡΠΈΠΈ ΠΠ°ΠΏΠΈΡΡ ΡΠ΅ΠΌΠΈΠ½Π°ΡΠ° | Π‘Π»Π°ΠΉΠ΄Ρ | ||||
| 07.11.2022 | Week09. Backpropogation | ΠΠ°ΠΏΠΈΡΡ ΡΠ΅ΠΌΠΈΠ½Π°ΡΠ° | Π‘Π»Π°ΠΉΠ΄Ρ | ΠΠ΅ΠΊΡΠΈΡ Π½Π΅ Π²Π΅Π»Π°ΡΡ ΠΏΠΎ ΠΏΡΠΈΡΠΈΠ½Π΅ Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Ρ, ΠΎΠ΄Π½Π°ΠΊΠΎ Π±ΡΠ» ΠΏΡΠΎΠ²Π΅Π΄ΡΠ½ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ ΡΠ΅ΠΌΠΈΠ½Π°Ρ ΠΏΠΎ backpropogation | |||
| 14.11.2022 | Week10. Dropout and Batchnorm | ΠΠ°ΠΏΠΈΡΡ Π»Π΅ΠΊΡΠΈΠΈ ΠΠ°ΠΏΠΈΡΡ ΡΠ΅ΠΌΠΈΠ½Π°ΡΠ° | Π‘Π»Π°ΠΉΠ΄Ρ | ||||
| 21.11.2022 | Week11. Embeddings and seq2seq model | ΠΠ°ΠΏΠΈΡΡ Π»Π΅ΠΊΡΠΈΠΈ ΠΠ°ΠΏΠΈΡΡ ΡΠ΅ΠΌΠΈΠ½Π°ΡΠ° | Π‘Π»Π°ΠΉΠ΄Ρ |
Prerequisites
Prerequisites are located here.
Literature:
- YSDA ML Book (Russian only)
- Probabilistic Machine Learning: An Introduction; English link, Π ΡΡΡΠΊΠΈΠΉ ΠΏΠ΅ΡΠ΅Π²ΠΎΠ΄
- Deep Learning Book: English link. ΠΠ΅ΡΠ²Π°Ρ ΡΠ°ΡΡΡ (Part I) ΠΊΡΠ°ΠΉΠ½Π΅ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄ΡΠ΅ΡΡΡ ΠΊ ΠΏΡΠΎΡΡΠ΅Π½ΠΈΡ.
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