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 für Master Medieninformatik, Informatik und MCI
Capacity: max. 50
Dates and Locations
- Lecture:
Date: Thu, 10-12 c.t.
Location: Thalkirchner Str.36 - Theoret. Hörsaal 151
First session: 20.04.2023 - Tutorial:
Date: Wed, 14-16 c.t.
Location: Thalkirchner Str.36 - Theoret. Hörsaal 151
First session: 05.05.2023
News
- 01.03.2023: You can now enroll for the course via Moodle.
- 07.02.2023: 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)
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
| Date | Topic |
|---|---|
| 20.04.2023 | Lecture 01: Organization & Introduction |
| 27.04.2022 | canceled |
| 04.05.2023 | Lecture 02: Supervised vs. Unsupervised Learning |
| 11.05.2023 | Guest Lecture by Prof. Chris Harrison from Carnegie Mellon University, USA |
| 18.05.2022 | Public holiday |
| 25.05.2023 | Lecture 04: Introduction Neural Networks Lecture 05: Advanced Neural Networks |
| 01.06.2023 | Lecture 06: Evaluating Neural Networks Lecture 07: Trainings Strategies |
| 08.06.2022 | Public holiday |
| 15.06.2023 | Lecture 08: Recurrent Neural Network (RNN) & Long Short-Term Memory (LSTM) |
| 22.06.2023 | Lecture 09: Generative Adversarial Networks (GANs) |
| 29.06.2023 | Lecture 10: Reinforcement Learning |
| 06.07.2023 | Lecture XX: TBA |
| 13.07.2023 | Open Discussion How to give a great project presentation Q'n'A: Exam preparation Individual Help for Projects |
| 20.07.2023 | Final Presentation |
Exercises
| Date | Topic |
|---|---|
| 05.05.2023 | Organization Lecture 03: Full Practical Neural Network Walkthrough |
| 10.05.2023 | Exercise 01: Recording your own data |
| 19.05.2023 | Public holiday |
| 17.05.2023 | Live Coding Session: Deploying Models to Mobile Devices (Android) Exercise 02: Clearing your data and training the first model (2 weeks) |
| 31.05.2023 | Project Ideation Exercise 03: Training an improved model based on a large dataset (1 week) |
| 09.06.2023 | Public holiday |
| 14.06.2023 | Project Pitches: Show Current Project Status |
| 21.06.2023 | Individual Help for Projects |
| 28.06.2023 | Individual Help for Projects |
| 05.07.2023 | Individual Help for Projects |
| 12.07.2023 | Individual Help for Projects |
| 19.07.2023 | Final Presentation |
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 TBA.
- 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.
