This is a repository containing seminars for the Skoltech's Machine Learning course (MA060018), which is held at Term 3, 2024.

• SEMINAR 1 (29.01): Ilya Trofimov - Machine learning on Titanic data
• SEMINAR 2 (30.01): Egor Shvetsov - Regression. Kernel Trick
• SEMINAR 3 (01.02): Ilya Trofimov - Classification
• SEMINAR 4 (06.02): Egor Shvetsov - Tree-based methods, Bagging, Random forest, Adaboost
• SEMINAR 5 (08.02): Alexander Marusov - Gradient Boosting
• SEMINAR 6 (12.02): Anton Dmitriev - Advanced classification: Imbalanced and Multi-label cases
• SEMINAR 7 (13.02): Milena Gazdieva - Model and Feature Selection
• SEMINAR 8 (15.02): Elizaveta Kovtun - Shallow Artificial Neural Networks
• SEMINAR 9 (19.02): Milena Gazdieva - Deep Artificial Neural Networks
• SEMINAR 10 (20.02): Petr Sokerin - Sequential Data, Attention Mechanism
• SEMINAR 11 (22.02): Leonid Erlygin - Gaussian Processes
• SEMINAR 12 (26.02): Kristian Kuznetsov - Dimensionality Reduction
• SEMINAR 13 (27.02): Nikita Gushchin - Anomaly Detection
• SEMINAR 14 (29.02): Petr Mokrov - Clustering

The course is a general introduction to machine learning (ML) and its applications. It covers fundamental topics in ML and describes the most important algorithmic basis and tools. It also provides important aspects of the algorithms’ applications. 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, and 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 ML methods and tune their hyper-parameters. Home assignments include the application of existing algorithms to solve data analysis problems. The students are assumed to be familiar with basic concepts in linear algebra, probability, real analysis, optimization, and python programming.

On completion of the course students are expected to:

• Have a good understanding of the fundamental issues and challenges of ML: data, model selection, model complexity among others;
• Have an understanding of the strengths and weaknesses of many popular ML approaches;
• Appreciate the basic underlying mathematical relationships within and across ML algorithms and the paradigms of supervised and unsupervised learning.
• Be able to design and implement various machine learning algorithms in a range of real-world applications.

The lectures of the course can be accessed via the link

If you have any questions/suggestions regarding this github repository or have found any bugs, please write to me at [email protected]