Lecture Artificial Intelligence in Interactive Systems Lecture Practical Machine Learning

Lecturer: Prof. Dr. Sven Mayer
Tutorials: Jesse Grootjen, Jan Leusmann
Hours per week: 2 (Lecture) + 2 (Tutorial)
ECTS credits: 6
Language: English
Module: 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)
Capacity: max. 100

• Syllabus
• Dates and Locations
• Requirements
• Lectures
• Exercises
• Exam

Syllabus

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. One group project (in groups up to four) has to be presented during the final presentation sessions. 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.

Dates and Locations

• Lecture:
  Date:Thu, 10-12 c.t.
  Location: Pettenkoferstr. 14, Kl. HS Physiologie (F1.08)
  
• Tutorial:
  Date: Thu, 16-18 c.t.
  Location: Pettenkoferstr. 14, Kl. HS Physiologie (F1.08)
  

Requirements

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

• Lecture Human-Computer Interaction
• Machine Learning, e.g. Machine Learning course
• Lecutre Introduction to Intelligent User Interfaces (IUI)

Additional Information

• https://neuralnetworksanddeeplearning.com/

Lectures

Exercises

Exam

The dates for the exams are:

• TBD
• Please register for the exam via Moodle.