Module Number

ML-4510 		Module Title

Practical Machine Learning 		Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Workload:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral presentation, written report, lab journal
Content

The practical course consists of finishing assigned tasks in small teams, autonomously
or under supervision. Study and exam performance are usually evaluated
based on active participation, a presentation of results and in written
reports.
Objectives

Students will gain practical experience in designing and programming methods
/ software /tools for ML. They will be able to use libraries and frameworks,
and will acquire knowledge or extend their knowledge of various programming
languages. By working together in groups, students obtain teamwork and collaboration
skills, and they will learn about project organization and presentation
techniques. Students will know about the strengths and weaknesses and about
the limitations of various methods for evaluating complex and high-dimensional
data, and will be able to describe and evaluate these methods.
Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Practical Course
P
o
4
6.0
tp, op
g
100
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Alle Dozenten
Literature

-
Last offered -
Planned for -
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV