{"id":13631258,"url":"https://github.com/girafe-ai/ml-course","last_synced_at":"2025-05-13T23:06:12.736Z","repository":{"id":37774054,"uuid":"168725504","full_name":"girafe-ai/ml-course","owner":"girafe-ai","description":"Open Machine Learning course","archived":false,"fork":false,"pushed_at":"2025-04-09T14:30:13.000Z","size":902675,"stargazers_count":2551,"open_issues_count":37,"forks_count":1168,"subscribers_count":69,"default_branch":"master","last_synced_at":"2025-04-09T15:41:01.579Z","etag":null,"topics":["computer-vision","course","deep-learning","machine-learning","materials","natural-language-processing","python","pytorch","reinforcement-learning","seminars"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/girafe-ai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-02-01T16:20:39.000Z","updated_at":"2025-04-09T15:39:06.000Z","dependencies_parsed_at":"2023-10-03T03:16:58.466Z","dependency_job_id":"31c7b2a7-e88e-4aa6-900f-c02d55d7a7a5","html_url":"https://github.com/girafe-ai/ml-course","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/girafe-ai%2Fml-course","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/girafe-ai%2Fml-course/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/girafe-ai%2Fml-course/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/girafe-ai%2Fml-course/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/girafe-ai","download_url":"https://codeload.github.com/girafe-ai/ml-course/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248243472,"owners_count":21071054,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","course","deep-learning","machine-learning","materials","natural-language-processing","python","pytorch","reinforcement-learning","seminars"],"created_at":"2024-08-01T22:02:18.097Z","updated_at":"2025-04-10T15:33:54.105Z","avatar_url":"https://github.com/girafe-ai.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook","Repos","📚 Project Purpose"],"sub_categories":["Machine Learning (Entry-Level)"],"readme":"[**Ссылка на ветку ML тренировок Яндекса 2023**](https://github.com/girafe-ai/ml-course/tree/23f_yandex_ml_trainings)\n\n# Machine Learning course\nFirst semester of girafe-ai Machine Learning course\n\n## Recordings and materials\n\n| Date   | Content                | Lecture video | Slides               | WarmUp test             | HW                  | Deadline          | Comments |\n|:------:|:-----------------------|:------------:|:------------:|:-----------------------:|:------------------------:|:----------------------:|:----------------------:|\n| 05.09.2022 | Week01. Intro, Naive Bayes and kNN. | [Запись лекции 2021](https://youtu.be/74Kd-rNxSm0) [Запись семинара 2021](https://youtu.be/bzCwHkO-YEk)| [Слайды](week0_01_naive_bayes/lect001_intro_knn_naive_bayes.pdf) | | [Assignment 01: kNN](homeworks/assignment0_01_knn) | 23.59 AOE, 03.10.2022 | *По техническим причинам запись лекции 2022 года не велась*\n| 12.09.2022 | extra Week. Linear algebra recap. | [Запись лекции](https://youtu.be/vKfdtHnXVEY?list=PLJR10EXrBaAv2vPy05qesewHv9JFc8ZjM) [Запись семинара 2022](https://youtu.be/Ha3pJJnt5YA?list=PLJR10EXrBaAv2vPy05qesewHv9JFc8ZjM)| [Слайды](week0_00_linear_algebra_recap/lecture00-linear_algebra_recap.pdf) |  | |  |  |\n| 19.09.2022     | Week02. Linear Regression. | [Запись лекции](https://youtu.be/imzlM4jRbD4?list=PLJR10EXrBaAv2vPy05qesewHv9JFc8ZjM) [Запись семинара 2022](https://youtu.be/LLGLeM3JKDQ?list=PLJR10EXrBaAv2vPy05qesewHv9JFc8ZjM) | [Слайды](week0_02_linear_reg/lect002_linear_regression.pdf) |  |  [Assignment 02: Linear Regression](homeworks/assignment0_02_lin_reg) | 23.59 AOE, 10.10.2022 |  |\n| 26.09.2022     | Week03. Linear Classification. | [Запись лекции](https://youtu.be/db1XU_WJHFs?list=PLJR10EXrBaAv2vPy05qesewHv9JFc8ZjM) [Запись семинара 2022](https://youtu.be/vSeETg1two8)   | [Слайды](week0_03_linear_classification/msai-ml_s21_lect003_logistic_regression.pdf)   |  | [Lab01: ML pipeline](https://github.com/girafe-ai/ml-course/tree/22f_basic/homeworks/lab01_ml_pipeline) | 23.59 AOE 10.11.2022 | \n| 03.10.2022     | Week04. SVM, PCA. | [Запись лекции](https://youtu.be/mlA-XxC9Ugg?list=PLJR10EXrBaAv2vPy05qesewHv9JFc8ZjM) [Запись семинара 2022](https://youtu.be/z-JqKoyHHRI?list=PLJR10EXrBaAv2vPy05qesewHv9JFc8ZjM)   | [Слайды](week0_04_svm_and_pca/lect004_svm_pca.pdf) |  |  [Assignment 03: SVM kernel](https://github.com/girafe-ai/ml-course/tree/22f_basic/homeworks/assignment0_03_svm) | 23.59 AOE, 24.10.2022 |  \n| 10.10.2022     | Week05. Trees and ensembles | [Запись лекции](https://youtu.be/kbNZsQj2eHk)   | [Слайды](week0_05_trees_and_ensembles/lect005_trees_and_ensembles_style.pdf) | | [Optional assignment 04: Tree from scratch](https://github.com/girafe-ai/ml-course/tree/22f_basic/homeworks/assignment0_04_tree) | 23.59 AOE, 22.12.2022 | Вместо семинара проходила контрольная работа | \n| 17.10.2022     | Week06. Gradient boosting | [Запись лекции](https://youtu.be/Y97xrRiLY1Q) [Запись семинара](https://youtu.be/4vo39B6M270)   | [Слайды](week0_06_boosting/week0_06_gradient_boosting.pdf) | | | |  | \n| 24.10.2022     | Week07. Разбор теста | [Запись разбора](https://youtu.be/YiO1N6yVJcg)    |  | | | | Вместо лекции были тест и разбор. | \n| 31.10.2022     | Week08. Intro into Deep Learning | [Запись лекции](https://youtu.be/G--msc2IR-Y) [Запись семинара](https://youtu.be/0WMAfRuFHy8)   | [Слайды](https://github.com/girafe-ai/ml-course/blob/22f_basic/week0_07_intro_to_DL/lect007_intro_to_dl_style.pdf) | | | |  | \n| 07.11.2022     | Week09. Backpropogation |  [Запись семинара](https://youtu.be/HGk5xQ0azdo)   | [Слайды]() | | | | Лекция не велась по причине болезни преподавателя, однако был проведён дополнительный семинар по backpropogation | \n| 14.11.2022     | Week10. Dropout and Batchnorm | [Запись лекции](https://youtu.be/UtEV_ILJTA0) [Запись семинара](https://youtu.be/tq-mmdsW5QI)   | [Слайды](https://github.com/girafe-ai/ml-course/blob/22f_basic/week0_08_dropout_batchnorm/lect008_deeplearning_part_2_style.pdf) | | | |  | \n| 21.11.2022     | Week11. Embeddings and seq2seq model | [Запись лекции](https://youtu.be/kUAnB_Leg6E) [Запись семинара](https://youtu.be/KOIEozoCQo0)   | [Слайды](https://github.com/girafe-ai/ml-course/blob/22f_basic/week0_09_embeddings_and_seq2seq/lect009_Language_models_and_RNN.pdf) | | | |  | \n\n\n\n## Prerequisites\nPrerequisites are located [here](./prerequisites.md).\n\n## Literature:\n1. [YSDA ML Book](https://academy.yandex.ru/dataschool/book) (Russian only)\n2. Probabilistic Machine Learning: An Introduction; [English link](https://probml.github.io/pml-book/book1.html), [Русский перевод](https://dmkpress.com/catalog/computer/data/978-5-93700-119-1/)\n3. Deep Learning Book: [English link](https://www.deeplearningbook.org/). Первая часть (Part I) крайне рекомендуется к прочтению.\n \nMore additional materials are available [here](https://github.com/girafe-ai/ml-course/blob/22f_basic/extra_materials.md)\n\n## Exam program:\nAvailable [here](./approximate_program.pdf)\n\n\n## Main authors:\n* Radoslav Neychev\n* Vladislav Goncharenko\n\n## Contributors:\n* Iurii Efimov\n* Nikolay Karpachev\n* Ivan Provilkov\n* Valery Marchenkov\n* Anastasia Ianina\n* Irina Rudenko\n* Fedor Ryabov\n\n## Acknowledgements:\nSpecial thanks to:\n* Stanislav Fedotov, YSDA for informative discussions, program verification and support.\n* Konstantiv Vorontsov\n* Vadim Strijov for teaching this course teachers\n* Just Heuristic\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgirafe-ai%2Fml-course","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgirafe-ai%2Fml-course","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgirafe-ai%2Fml-course/lists"}