ML2021_seminars
This is a repository containing the seminars for the Skoltech's Machine Learning course (MA060018), which is held at Term 3, 2021.
The list of the current seminars published (will be updated with time):
- SEMINAR 1 (02.02): Ilya Trofimov - Introduction to Python and Machine Learning
- SEMINAR 2 (04.02): Alexey Zaycev - Regression
- SEMINAR 3 (05.02): Ilya Trofimov - Classification
- SEMINAR 4 (09.02): Andrey Lange - SVM
- SEMINAR 5 (11.02): Andrey Lange - Trees, Bagging, Random Forest
- SEMINAR 6 (12.02): Denis Volkhonskiy - AdaBoost
- SEMINAR 7 (16.02): Andrey Lange - Gradient Boosting
- SEMINAR 8 (18.02): Nina Mazyavkina - Imabalanced and Multi-Class Classification
- SEMINAR 9 (19.02): Oleg Voynov - Shallow Artificial Neural Networks
- SEMINAR 10 (20.02): Yermek Kapushev - Model and Feature Selection
- SEMINAR 11 (25.02): Oleg Voynov - Deep Learning
- SEMINAR 12 (02.03): Nikita Balabin - Bayesian Machine Learning
- SEMINAR 13 (04.03): Yermek Kapushev - Gaussian Processes
- SEMINAR 14 (05.03): Ekaterina Kondrateva - Dimensionality Reduction
- SEMINAR 15 (09.03): Nikita Klyuchnikov - Anomaly Detection
Course Description:
The course is a general introduction to machine learning (ML) and its applications. It covers fundamental modern topics in ML, and describes the most important theoretical basis and tools necessary to investigate properties of algorithms and justify their usage. It also provides important aspects of the algorithms’ applications, illustrated using real-world problems. The course starts with an overview of canonical ML applications and problems, learning scenarios, etc. Next, we discuss in depth fundamental ML algorithms for classification, regression, clustering, etc., their properties as well as their practical applications. The last part of the course is devoted to advanced ML topics such as Gaussian processes, neural networks. Within practical sections, we show how to use the methods above to crack various real-world problems. Home assignments include application of existing algorithms to solve applied industrial problems, development of modifications of ML algorithms. The students are assumed to be familiar with basic concepts in linear algebra, probability, calculus, optimization and python programming.
Full course syllabus:
The course syllabus and the other helpful information can be found in Canvas through the link.
Lectures:
The lectures of the course can accessed via the link.
The list of the Teaching Assistants:
- Alexander Korotin
- Nikita Balabin
- Egor Shvetsov
- Evgenia Romanenkova
- Nina Mazyavkina
- Ruslan Rakhimov
You can contact the TAs via Canvas.
Contact regarding this github repo:
If you have any questions/suggestions regarding this githup repository or have found any bugs, please write to me at Nina.Mazyavkina@skoltech.ru