Interdisciplinary Introduction to Machine Learning
Interdisciplinary Introduction to Machine Learning - Theory (10SMSTS-201)
Description
This course on machine learning is designed to provide a comprehensive understanding from a multi-disciplinary perspective. Throughout the course, we will delve into the algorithms and techniques that constitute machine learning, while also considering its applications and limitations across various fields – Medicine, Law, Linguistics, Physical Sciences, and Robotics, to name a few.
The aim is to equip students with the knowledge to critically assess the suitability of machine learning solutions for different types of challenges.
By the end of this course, students should have a nuanced understanding of machine learning's capabilities and restrictions, informed by examples across multiple sectors.
Target Group
MA
ECTS Credits
3
Course catalogue
More information about the course
Interdisciplinary Introduction to Machine Learning - Exercises (10SMSTS-200)
Description
In this module, students have the opportunity to engage in exercises that address real-world problems in various disciplines, providing a hands-on experience with machine learning methodology.
This module is designed in combination with the corresponding module: "Interdisciplinary Introduction to Machine Learning - Theory (10SMSTS-201)" and should only be booked together with it. Students will be assigned exercises (Python programming and/or non-programming exercises) for each lecture of the course 10SMSTS-201.
Target Group
MA
ECTS Credits
2
Course catalogue
More information about the course
