Vorlesung Artificial Intelligence in Interactive Systems Vorlesung Practical Machine Learning

Dozent: Prof. Dr. Sven Mayer
Übungsleitung: Jesse Grootjen, Jan Leusmann
Umfang: 2 SWS Vorlesung, 2 SWS Übung
ECTS credits: 6
Sprache: Englisch
Modul: Vertiefende Themen: WP 7, WP 10, WP 13 (MA INF PStO 2022); WP 1, WP 7, WP 19, WP 26 (MA MI PStO 2022); WP 1, WP 4 (MA MCI PStO 2022); WP 45, WP 49, WP 50 (BA INF PStO 2022); WP 23, WP 27, WP 28 (BA MI PStO 2022)
Kapazität: max. 100

• Lehrplan
• Termine und Ort
• Empfohlene Vorkenntnisse
• Vorlesungen
• Übungen
• Klausur

Lehrplan

The goal of this course is to teach the theoretical and practical skills needed to build novel intelligent user interfaces. In detail, the course teaches the fundamental steps of training, deploying, and testing novel intelligent user interfaces using machine learning (ML). Here, we will focus on neuronal networks while using traditional machine learning approaches (e.g., SVN, Random Forest) only as a baseline. During the course, students will learn how to collect data, train ML models, and evaluate the new models based on the extended User-Centered Design process for deep learning.

Over the course of the semester, students will build novel interfaces and present intermediate milestones throughout the tutorials. Before developing a new novel interface, the tutorials will also be used to learn the lecture topics' practical side using hands-on exercises. Here, students will learn how to train, deploy, and validate models based on a set of showcase examples.

In summary, this lecture is a practical oriented course that teaches the theoretical and practical skills to train neuronal networks to build intelligent user interfaces from scratch.

Termine und Ort

• Vorlesung:
  Termin: Do, 10-12 c.t.
  Ort: Pettenkoferstr. 14, Kl. HS Physiologie (F1.08)
  
• Übung:
  Termin:Fri 10-12 c.t.
  Ort: Pettenkoferstr. 14, Kl. HS Physiologie (F1.08)
  

Empfohlene Vorkenntnisse

The course is designed for senior master students who have taken those following courses (or have equivalent knowledge):

• Vorlesung Mensch-Maschine-Interaktion
• Machine Learning, e.g. Machine Learning course
• Vorlesung Introduction to Intelligent User Interfaces (IUI)

Additional Information

• https://neuralnetworksanddeeplearning.com/

Vorlesungen

Übungen

Klausur

Die Termine für die Prüfungen sind wie folgt:

• TBD
• Die Anmeldung zur Prüfung erfolgt auf Moodle.