Discriminative Methods in Machine Learning

2022/2023

Academic Year

ENG

Instruction in English

8

ECTS credits

Delivered by:

School of Data Analysis and Artificial Intelligence

Type:

Elective course

When:

2 year, 1 semester

Instructor

Kertesz-Farkas, Attila

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.