Lecture: Deep Learning - Details

General information

Course name	Lecture: Deep Learning
Course number	INF-0315
Semester	SS 2022
Current number of participants	74
Home institute	Embedded Intelligence for Health Care and Wellbeing
participating institutes	Institut für Informatik
Courses type	Lecture in category Teaching
First date	Wednesday, 27.04.2022 14:00 - 15:30, Room: (2045N)
Pre-requisites	Knowledge from mathematics lectures should be present.
Performance record	Klausur am Ende des Semesters / Exam at the end of the semester
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile	Yes
Hauptunterrichtssprache	englisch
Literaturhinweise	Ian Goodfellow; Yoshua Bengio; Aaron Courville (2016). Deep Learning. Cambridge, Massachusetts: MIT Press. Further literature is going to be announced in the first lecture of the course.
ECTS points	5

Lecturers

• Dr.-Ing. Shahin Amiriparian
• Prof.Dr. Björn Schuller
• Manuel Milling
• Maurice Gerczuk
• Lukas Christ
• Andreas Triantafyllopoulos

Rooms and times

(2045N)

Wednesday: 14:00 - 15:30, weekly (14x)

Fields of study

• Fakultät für Angewandte Informatik > Informatik
• Fakultät für Angewandte Informatik

Comment/Description

The course Deep Learning covers the fundamentals and applications of Machine Learning using Deep Neural Networks.

The Lecture includes a historical outline of Neural Network developments, covering all aspects from basic concepts to complex models. The course covers Neural Network architectures that are suitable for a variety of data types and signals from different domains, such as audio, speech, vision, and text.

In the Tutorials, students will be familiarised with the latest Deep Learning toolkits such as Tensorflow and Keras and learn how to train and evaluate Deep Neural Networks for different applications.