Lecture Practical Machine Learning

Lecturer: Prof. Dr. Sven Mayer
Tutorials: Jesse Grootjen, Luke Haliburton
Hours per week: 2 (Lecture) + 2 (Tutorial)
ECTS credits: 6
Language: English
Module: Vertiefende Themen für Master Medieninformatik, Informatik und MCI
(MA MI PStO 2022 (Start WiSe)) / (MA MI PStO 2022 (Start SoSe))
(MA MCI PStO 2022 (Start WiSe)) / (MA MCI PStO 2022 (Start SoSe))
Capacity: max. 100

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

Dates and Locations

• Lecture:
  Date:Thu, 10-12 c.t.
  Location: Thalkirchner Str.36 - Theoret. Hörsaal 151
  First session: 18.04.2024
• Tutorial:
  Date: Thu, 16-18 c.t.
  Location: Thalkirchner Str.36 - Theoret. Hörsaal 151
  First session: 18.04.2024

News

• 07.02.2024: This page is still under development, all content may be subject to change.

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/

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.

Lectures

Exercises

Exam

The exam will consist of two parts:

• Your practical project including the final presentation (1/2 of the final grade)
• An exam about the content of the lectures and exercises (1/2 of the final grade)
• Note: To pass the course, both parts must be passed independently of each other.

The dates for the exams are:

• The exams will probably take place on Tuesday 23.07.2024 from 12:00 - 14:00 in Geschwister-Scholl-Platz 01 - Room E 004.
• The final presentation of the practical projects will take place on TBA during the tutorial and lecture times.
• Please register for the exam via Moodle.