Vorlesung und Übung: Neuroinformatics (Lecture + Tutorial) - Details

Allgemeine Informationen

Veranstaltungsname	Vorlesung und Übung: Neuroinformatics (Lecture + Tutorial)
Untertitel
Veranstaltungsnummer	8.3047
Semester	WiSe 2021/22
Aktuelle Anzahl der Teilnehmenden	197
erwartete Teilnehmendenanzahl	200
Heimat-Einrichtung	LE Cognitive Science
Veranstaltungstyp	Vorlesung und Übung in der Kategorie Offizielle Lehrveranstaltungen
Erster Termin	Donnerstag, 21.10.2021 14:00 - 16:00, Ort: 66/E34
Art/Form
Teilnehmende	ab 3. Semester
SWS	6
Sprache	Englisch
ECTS-Punkte	12

Lehrende

• Prof. Dr. Gordon Pipa
• M. Sc. Sabrina Frohn
• Laura Krieger

Räume und Zeiten

66/E34

Donnerstag: 14:00 - 16:00, wöchentlich (14x)

Freitag: 12:00 - 14:00, wöchentlich (14x)

32/102

Freitag: 10:00 - 12:00, wöchentlich (14x)

Studienbereiche

• Veranstaltungen > Data Science
• Veranstaltungen > Cognitive Science > Bachelor-Programm
• Schnupperstudium > Schnupper Uni > Cognitive Science

Modulzuordnungen

• Bachelor of Science Informatik > CS-BWP-NI - Neuroinformatik, gültig ab WiSe 2019/20
• Bachelor of Science Mathematik > CS-BWP-NI - Neuroinformatik, gültig ab WiSe 2019/20
• Bachelor of Science Cognitive Science > CS-BWP-NI - Neuroinformatik, gültig ab WiSe 2019/20
• Master of Science Mathematik > CS-BWP-NI - Neuroinformatik, gültig ab WiSe 2019/20
• Bachelor of Science Informatik > CS-BP-NI - Neuroinformatik, gültig ab WiSe 2019/20
• Bachelor of Science Mathematik > CS-BP-NI - Neuroinformatik, gültig ab WiSe 2019/20
• Bachelor of Science Cognitive Science > CS-BP-NI - Neuroinformatik, gültig ab WiSe 2019/20
• Bachelor of Science Umweltsystemwissenschaft > CS-BP-NI - Neuroinformatik, gültig ab WiSe 2019/20
• Master of Science Mathematik > CS-BP-NI - Neuroinformatik, gültig ab WiSe 2019/20

Kommentar/Beschreibung

Prerequisites: Angewandte Mathematik, oder Lineare Algebra, Analysis

In this lecture, we will discuss cutting edge approaches from the field of Neuroinformatics. The aim of the lecture is to get the students familiar with the concept of modelling and abstracting data, and the up to date knowledge about computational processes in the brain. After a short introduction that covers probability theory, and linear models for regression and classification, we will start a journey through the fields of graphical models and liquid computing. In the last part of the lecture we will conclude with an outlook to self-organization with the purpose to optimize information processing in complex systems like the brain. To link the knowledge acquired in this course with scientific question every second lecture a 30 min 3W session is offered. The three big W are: why should I learn this / what for can I use it / how can it be important my bachelor and master thesis . The lecture will be supplemented by a block seminar on decoding neuronal activity at the beginning of the semester break. This course is intended for Bachelor students in their third term and for master students as a compulsory module.