|
Instructors: Johannes Magnus Heuel; Prof. Dr. Alexander Szimayer
Event type:
Interactive class
Displayed in timetable as:
Hours per week:
3
Credits:
6,0
Language of instruction:
English
Min. | Max. participants:
- | 45
Comments/contents:
This course provides an overview of multiple machine learning techniques. The methods will be introduced on a theoretical level. Afterwards, students implement these techniques using the programming language Python. The course enables students to plan and develop research projects on their own.
The topics covered include:
- Model Selection and Evaluation
- Linear Models
- Decision Trees
- Support Vector Machines
- Ensemble Learning
- Clustering
- Neural Networks and Outlook
Learning objectives:
In this course, students acquire both a sound theoretical background of machine learning techniques and the ability to implement these methods within an empirical project using Python.
Didactic concept:
The weekly lectures provide a rigorous introduction to contemporary machine learning techniques. In the bi-weekly tutorials, course participants learn how to implement and apply these techniques. Research projects give students the opportunity to implement the techniques within an empirical research project in groups.
Literature:
- Zhou, Zhi-Hua (2021): Machine Learning, 1st ed.
Additional examination information:
Project report and presentation, oral examination.
|
