Obligatory courses for 2nd year Machine Learning (course group defined by Faculty of Mathematics, Informatics, and Mechanics)
Course group schedules
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2024Z - Winter semester 2024/25 2024 - Academic year 2024/25 2025Z - Winter semester 2025/26 2025L - Summer semester 2025/26 2025 - Academic year 2025/26 (there could be semester, trimester or one-year classes) |
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| 2024Z | 2024 | 2025Z | 2025L | 2025 | |||||||
| 1000-319bINT | n/a |
 
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Classes
Academic year 2024/25
Groups
Brief description
Obligatory vocational internship for students of machine learning programme. |
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| 1000-319bBML |
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Classes
Winter semester 2024/25
Groups
Brief description
The goal of this course is to build the theoretical foundation and practical skills necessary to use machine learning algorithms and techniques at a large scale. We will discuss the architecture of modern large-scale computing infrastructure (cloud datacenters, and AI and HPC supercomputers). We will present methods for distributing computations across these clusters and the fundamental algorithmic models used to estimate performance. Using examples of typical ML algorithms (decision trees, neural network training), we will demonstrate the theoretical and practical challenges of using them at the scale of a few to several hundred machines. Next, we will cover the challenges of training and using large-scale language models (LLM). The course will conclude by presenting the primary problems of using ML models in large-scale production environments. |
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| 1000-319bEML |
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n/a |
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n/a |
Classes
Winter semester 2024/25
Groups
Brief description
The goal of the course is to learn about concepts, methods and techniques for explaining complex machine learning models. Predictive models are becoming increasingly complex, tree ensembles, deep neural networks are models with thousands of parameters. For models with such dimensionality, it is easy to lose track of what the model has learned. During this course we will discuss tools for analysing the structure of a model treated as a black box, and analysing the predictions from the model. This will allow us to increase confidence in the model, improve model performance, and be able to extract useful knowledge from the model. We will learn about the most popular explanatory methods, discuss their strengths and weaknesses so that the class participant has the necessary competences to further explore the literature in this area. |
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| 1000-319bTML | n/a |
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Classes
Academic year 2024/25
Groups
Brief description
During the course, both scientific and implementation-oriented projects are carried out. The first sessions are dedicated to presenting topics, followed by team formation (comprising 2 to 4 individuals) and assignment of topics. Throughout each semester, each team prepares three presentations on the progress made. The assessment in this course is influenced by the supervisor's feedback, as well as the final outcome in the form of a report, manuscript, or repository. |
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