Lecture: Machine Learning (Lecture) (CS-BP-NI, CS-BWP-AI, CS-BWP-INF, CS-BWP-NI) - Details

General information

Course name	Lecture: Machine Learning (Lecture) (CS-BP-NI, CS-BWP-AI, CS-BWP-INF, CS-BWP-NI)
Subtitle
Course number	8.3072
Semester	SoSe 2020
Current number of participants	237
Home institute	LE Cognitive Science
Courses type	Lecture in category Offizielle Lehrveranstaltungen
First date	Wednesday, 15.04.2020 10:00 - 12:00, Room: 01/E01
Type/Form
Participants	ab 4. Semester
SWS	4
Primary Language of Instruction	Englisch / English
ECTS points	8

Lecturers

• Prof. Dr.-Ing. Gunther Heidemann

Tutors

• David Neufeld
• Emilia Arens
• Emma Seeger

Rooms and times

01/E01

Wednesday: 10:00 - 12:00, weekly (12x)

Thursday: 10:00 - 12:00, weekly (12x)

01/B01

Wednesday: 10:00 - 12:00, weekly (1x)

Friday, 17.04.2020 10:00 - 12:00
Friday, 17.04.2020 14:00 - 16:00
Monday, 20.04.2020 10:00 - 12:00
Monday, 20.04.2020 14:00 - 16:00

Fields of study

• Veranstaltungen > Cognitive Science > Bachelor-Programm
• Courses Open to Exchange Students > Human Sciences (e.g. Cognitive Science, Psychology)
• Schnupperstudium > Schnupper Uni > Cognitive Science
• Veranstaltungen > Informatik > Importmodule
• Uni für Alle > Cognitive Science

Module assignments

• Bachelor of Science Informatik > INF-INF-KI-9-L - Machine Learning (Specialization KI)
• Bachelor of Science Eingebettete Softwaresysteme > INF-INF-KI-9-L - Machine Learning (Specialization KI)
• Bachelor of Science Umweltsystemwissenschaft > INF-INF-KI-9-L - Machine Learning (Specialization KI)
• Master of Science Informatik > INF-INF-KI-9-L - Machine Learning (Specialization KI)
• Master of Science Mathematik > INF-INF-KI-9-L - Machine Learning (Specialization KI)
• Master of Science Geoinformatik > INF-INF-KI-9-L - Machine Learning (Specialization KI)
• Master of Science Umweltsysteme und Ressourcenmanagement > INF-INF-KI-9-L - Machine Learning (Specialization KI)
• Master Lehramt an Gymnasien Informatik > INF-INF-KI-9-L - Machine Learning (Specialization KI)
• Erweiterungsmaster Lehramt an Gymnasien Informatik > INF-INF-KI-9-L - Machine Learning (Specialization KI)
• 2-Fächer-Bachelor Informatik > INF-INF-KI-9-L - Machine Learning (Specialization KI)
• 2-Fächer-Bachelor Professionalisierung > INF-INF-KI-9-L - Machine Learning (Specialization KI)
• Bachelor of Science Informatik > CS-BP-NI - Neuroinformatics valid from WiSe 2019/20
• Bachelor of Science Mathematik > CS-BP-NI - Neuroinformatics valid from WiSe 2019/20
• Bachelor of Science Cognitive Science > CS-BP-NI - Neuroinformatics valid from WiSe 2019/20
• Bachelor of Science Umweltsystemwissenschaft > CS-BP-NI - Neuroinformatics valid from WiSe 2019/20
• Master of Science Mathematik > CS-BP-NI - Neuroinformatics valid from WiSe 2019/20
• Bachelor of Science Informatik > CS-BWP-NI - Neuroinformatics valid from WiSe 2019/20
• Bachelor of Science Mathematik > CS-BWP-NI - Neuroinformatics valid from WiSe 2019/20
• Bachelor of Science Cognitive Science > CS-BWP-NI - Neuroinformatics valid from WiSe 2019/20
• Master of Science Mathematik > CS-BWP-NI - Neuroinformatics valid from WiSe 2019/20
• Bachelor of Science Informatik > CS-BWP-AI - Artificial Intelligence valid from WiSe 2019/20
• Bachelor of Science Mathematik > CS-BWP-AI - Artificial Intelligence valid from WiSe 2019/20
• Bachelor of Science Cognitive Science > CS-BWP-AI - Artificial Intelligence valid from WiSe 2019/20
• Master of Science Mathematik > CS-BWP-AI - Artificial Intelligence valid from WiSe 2019/20
• Bachelor of Science Cognitive Science > CS-BWP-INF - Informatics valid from WiSe 2019/20

Comment/Description

Prerequisites: None

Being a mainly academic topic about 20 years ago, Machine Learning has become a discipline of major impact on both science and engineering by today. This course introduces the basics of Machine Learning and Data Mining. Major topics are concept learning, decision trees, problems of data in high dimensional representations, clustering algorithms, linear and nonlinear dimension reduction, artificial neural networks (e.g. multilayer perceptrons, RBF networks, self-organizing maps), classification methods, reinforcement learning, modeling uncertainty and temporal probability models.