Obligatory courses for 2nd year Machine Learning (course group defined by Faculty of Mathematics, Informatics, and Mechanics)

Winter semester 2023/24

• Classes - 30 hours
• Lab - 15 hours
• Lecture - 15 hours

Winter semester 2024/25

• Classes - 30 hours
• Lab - 15 hours
• Lecture - 15 hours

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.

Winter semester 2023/24

• Lab - 30 hours
• Lecture - 30 hours

Winter semester 2024/25

• Lab - 30 hours
• Lecture - 30 hours

During this class we will present techniques and tools for processing Big data. We will focus on the ones useful for machine learning practitioners. We will show the most important models and basic algorithmic techniques. We will cover how to analyze algorithms that process large data on clusters. Finally, we will introduce typical optimizations that can be useful in machine learning applications like linear regression, clustering, decision trees or neural networks.