Postgraduate course
2022/2023
Discriminative Methods in Machine Learning
Type:
Elective course
Area of studies:
Informatics and Computer Engineering
Delivered by:
School of Data Analysis and Artificial Intelligence
Where:
Faculty of Computer Science
When:
2 year, 1 semester
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Attila Kertesz-Farkas
Language:
English
ECTS credits:
8
Contact hours:
36
Course Syllabus
Abstract
This course gives an introduction to the most popular discriminative and differentiable machine learning methods, which are used in supervised learning. After completing the study of the discipline, the PhD student should have knowledge about modern discriminative methods such as deep convolutional learning techniques, kernel machines, limitations of learning methods and standard definitions such as overfitting, regularization, etc., knowledge about ongoing developments in Machine Learning, hands-on experience with large scale machine learning problems, knowledge about how to design and develop machine learning programs using programming language Python, and be able to think critically with real data.