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# 2022 Introduction to Machine Learning and Data Analysis

This is a repo for a 2022 course at the Department of Cosmic Research, MSU. 
The course covers the very basic concepts of ML, it is obligatory for 4th year students, and for MS students it is an elective course.

# General info
- The course consists of 12 lectures and 12 seminars and 5 homeworks.
- All lectures and seminars will be held **online** via Zoom and probably recorded (Zoom screen capture).
- PDFs with lecture slides will be published here: https://disk.yandex.ru/d/ttnWpSXcSvmAtA
- All homeworks are to be submitted **online**.

**!!There will be no exams, final mark is based on homeworks only!!**

# Communication
- email `cosmic.research.ml@yandex.ru`
- telegram news channel: https://t.me/+mlstxc99aQM2NWEy
- telegram chat room: https://t.me/+g5h-t9Q20K04NDZi

# Lectures and seminars
The timetable may be change.
- Lectures: 09:00 - 10:20 (UTC+3, Europe/Moscow)
- Seminars: 10:40 - 12:00 (UTC+3, Europe/Moscow)

Presentations and videos can be downloaded from here: https://disk.yandex.ru/d/ttnWpSXcSvmAtA

| #  | Date | Lecture | Seminar | Homework |
| -- | ---- | ------- | ------- | -------- |
| 1  | 2022-09-09 | Introduction to the topic | How to manage this course | HW#1 |
| 2  | 2022-09-16 | Metric Algorithms, kNN | Classification Quality Metrics | - |
| 3  | 2022-09-23 | Decision Trees | How to tune hyperparameters | - |
| 4  | 2022-09-30 | Linear Models | Invited speaker: Intro to Data Visualization | HW#2 |
| 5  | 2022-10-07 | SVM | PCA and SVD | - |
| 6  | 2022-10-14 | Regularized Linear Models | Invited Speaker: A/B Tests and Metrics  | HW#3 |
| 7  | 2022-10-21 | Time Series | Practical Analytics: when to alert | - |
| 8  | 2022-10-28| Text Classification | Practical Analytics: SQL | HW#4 |
| 9  | 2022-11-11 | Bayes approach, EM | Practical Analytics | - |
| 10 | 2022-11-18 | Clustering| Invited Speaker: Seismic Fault Detection | HW#5 |
| 11 | 2022-11-25 | Ranking | TBA | - |
| 12 | 2022-12-02 | Gradient Boosting | TBA | - |
| 13 | 2022-12-09| No Lecture Here | TBA | - |

# Homeworks and deadlines
- Each homework has its deadline. Submission after deadline will reduce points
- Submission after the deadline: each score is multiplied by 0.5

| #   | Name | Date Published | Deadline |  Link |
| --- | ---- | -------------- | -------- | -- |
| 1  | HW#1 | 2022-09-09 | 2022-10-06 23:00:00 +03:00 | https://github.com/cosmic-research-ml-edu/intro_ml_2022/blob/main/homeworks/hw01/lab01.ipynb |
| 2  | HW#2 | 2022-09-30 | 2022-10-20 23:00:00 +03:00 | https://github.com/cosmic-research-ml-edu/intro_ml_2022/blob/main/homeworks/hw02/lab02.ipynb |
| 3  | HW#3 | 2022-10-14 | 2022-11-03 23:00:00 +03:00 | https://github.com/cosmic-research-ml-edu/intro_ml_2022/blob/main/homeworks/hw03/lab03.ipynb |
| 4  | HW#4 | 2022-11-01 | 2022-11-24 23:00:00 +03:00 | https://github.com/cosmic-research-ml-edu/intro_ml_2022/blob/main/homeworks/hw04/lab04.ipynb |
| 5  | HW#5 | 2022-11-18 | 2022-12-08 23:00:00 +03:00 | https://github.com/cosmic-research-ml-edu/intro_ml_2022/blob/main/homeworks/hw05/lab05.ipynb |

## Acknowledgements

This course was inspired by
-  The course by Konstantin Vorontsov from the Coursera [[link]](https://ru.coursera.org/learn/vvedenie-mashinnoe-obuchenie)
- MIT OpenCourseWare Machine Learning course: **Rohit Singh, Tommi Jaakkola, and Ali Mohammad. 6.867 Machine Learning. Fall 2006. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.**
- Data Mining In Action: [[link]](https://www.youtube.com/channel/UCop3CelRVvrchG5lsPyxvHg/videos)
- Author's personal experience in Data Science obtained from Yandex School of Data Science [[link]](https://yandexdataschool.com/), working at Yandex [[link]](https://yandex.ru/company/), Lensa [[link]](https://lensa.com/about-us) and various ML-projects.

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