ML2020_seminars

This is a repository containing seminars for Machine Learning course (MA060018), which is held at Term 3, 2020.

The list of the current seminars published (will be updated with time):

• SEMINAR 1 (04.02): Ilya Trofimov - Introduction to Python and Machine Learning
• SEMINAR 2 (06.02): Alexey Artemov - Regression, Kernel trick
• SEMINAR 3 (07.02): Ilya Trofimov - Classification
• SEMINAR 4 (11.02): Andrey Lange - Support Vector Machines [updated on 05.02]
• SEMINAR 5 (13.02): Andrey Lange - Tree-based methods, Bagging, Random forest
• SEMINAR 6 (14.02): Part 1: Nina Mazyavkina - Advanced classification: Imbalanced and Multi-class cases; Part2: Alexander Korotin - Theoretical Seminar.
• SEMINAR 7 (20.02): Denis Volkhonskiy - Statistical Analysis of Bagging. AdaBoost. Stacked generalization
• SEMINAR 8 (20.02): Andrey Lange - Gradient Boosting and Gradient Boosting Decision Trees
• SEMINAR 9 (21.02): Rodrigo Rivera-Castro, Yermek Kapushev - Model and feature selection, sensitivity analysis
• SEMINAR 10 (27.02): Evgeny Egorov - Bayesian ML
• SEMINAR 11 (28.02): Yermek Kapushev - Gaussian Processes
• SEMINAR 12 (03.03): Oleg Voynov - Shallow Artificial Neural Networks
• SEMINAR 13 (05.03): Oleg Voynov - Deep ANNs
• SEMINAR 14 (06.03): Ekaterina Kondrateva - Dimensionality Reduction
• SEMINAR 15 (10.03): Nikita Klyuchnikov - Anomaly Detection [updated on 24.03]
• SEMINAR 16 (12.03): Ekaterina Kondrateva - Clustering
• SEMINAR 17 (13.03): Ilya Trofimov - Active Learning
• SEMINAR 18 (22.03): Rodrigo Rivera-Castro - Time-Series

Course Description:

The course is a general introduction to machine learning (ML) 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 the 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. and introduction into theoretical foundations of ML. We present the most novel theoretical tools and concepts trying to be as succinct as possible. Then we discuss in-depth fundamental ML algorithms for classification, regression, boosting, etc., their properties as well as their practical applications. The last part of the course is devoted to advanced ML topics such that neural networks, anomaly detection, etc. Within practical sections, we show how to use the methods above to crack various real-world problems. Home assignments include the application of existing algorithms to solve applied industrial problems, the development of modifications of ML algorithms, as well as some theoretical exercises. The students are assumed to be familiar with basic concepts in linear algebra, probability, and real analysis.

Full course syllabus:

Course textbooks:

How to use Google Collab:

The instructions on how to open seminars' notebooks in Google Colaboratory: link

The list of the Teaching Assistants:

• Alexander Korotin
• Ekaterina Kondrateva
• Rodrigo Rivera-Castro
• Vage Egiazarian
• Savva Ignatyev
• Alexnder Safin
• Nina Mazyavkina

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 [email protected]