This is a repository containing the lectures for the Machine Learning course (MA060018), which is held at Term 3, 2020.
- LECTURE 1 (04.02) - Course Description and Rules, Overview of Data Analysis, Introduction to ML
- LECTURE 2 (06.02) - Regression
- LECTURE 3 (07.02) - Classification [updated on 11.02]
- LECTURE 4 (11.02) - Support Vector Machines
- LECTURE 5 (13.02) - Decision Trees
- LECTURE 6 (14.02) - Imbalanced Classification. Multiclass Classification. Nonparametric Estimation [updated on 18.02]
- LECTURE 7 (18.02) - Ensembles. Stacked generalization. AdaBoost
- LECTURE 8 (18.02) - Gradient Boosting
- LECTURE 9 (21.02) - Models Selection and Feature Selection
- LECTURE 10.1 (25.02) - Kernel Methods: Theory
- LECTURE 10.2 (25.02) - Graphs, GANs, Medical
- LECTURE 11 (27.02) - Bayesian Methods
- LECTURE 12 (28.02) - Gaussian Processes
- LECTURE 13 (03.02) - Neural Networks
- LECTURE 14 (05.03) - Deep Neural Networks
- LECTURE 15 (06.03) - Dimensionality Reduction
- LECTURE 16 (10.03) - Anomaly Detection
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.
The seminars of the course can accessed via the link: link
- Alexander Korotin
- Ekaterina Kondrateva
- Rodrigo Rivera-Castro
- Vage Egiazarian
- Savva Ignatyev
- Alexnder Safin
- Nina Mazyavkina
You can contact the TAs via Canvas.
If you have any questions/suggestions regarding this githup repository or have found any bugs, please write to me at Nina.Mazyavkina