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Mathematical Foundations of Machine Learning

Mathematical Foundations of Machine Learning

We continue a series of educational courses on knowledge-intensive technologies and the use of AI and ML in materials science. We invite you to the fourth course of the BOOSTalent project "Mathematical Foundations of Machine Learning".

This course is dedicated to the fundamental mathematical concepts essential for understanding machine learning.At its core, any machine learning algorithm involves the minimization or maximization of certain parameters.
The first lecture will focus on mathematical optimization, providing insights into its methods and addressing challenges encountered when searching for the minimum of a function.Following this lecture, you will be acquainted with "Stochastic Gradient Descent", "Adam", etc.
Next, we will shift our focus to probability and statistics.This segment aims to provide you with an understanding of generative models.
Additionally, you will enhance your toolkit with Bayes' theorem, basic statistical analysis, and simple text classification.

You can meet the lecturer and learn the course structure from this short video:

The course is free. All students will receive certificates with the amount of ECTS credits on completing the course (0,5 credit)

The Deep Tech courses are being delivered to you by the Kyiv Academic University as part of the BOOSTalent project.

If you have any questions, please fill out the form