Lecture Practical Machine Learning
Lecturer: Prof. Dr. Sven Mayer
Tutorials: Jesse Grootjen, Maximiliane Windl
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: Pettenkoferstr. 14, Kl. HS Physiologie (F1.08)
First session: 28.04.2022 - Tutorial:
Date: Fri, 10-12 c.t.
Location: Pettenkoferstr. 14, Kl. HS Physiologie (F1.08)
First session: 06.05.2022
News
- 17.02.2022: 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 |
|---|---|
| 28.04.2022 | Lecture 01: Organization & Introduction |
| 05.05.2022 | Lecture 02: Supervised vs. Unsupervised Learning Lecture 03: Full Practical Neural Network Walkthrough |
| 12.05.2022 | canceled |
| 19.05.2022 | Lecture 04: Introduction Neural Networks Lecture 05: Advanced Neural Networks |
| 26.05.2022 | canceled - public holiday |
| 02.06.2022 | Lecture 06: Evaluating Neural Networks Lecture 07: Trainings Strategies |
| 09.06.2022 | Lecture 08: Recurrent Neural Network (RNN) & Long Short-Term Memory (LSTM) |
| 16.06.2022 | canceled - public holiday |
| 23.06.2022 | Lecture 09: Generative Adversarial Networks (GANs) |
| 30.06.2022 | Lecture 10: Reinforcement Learning |
| 07.07.2022 | canceled |
| 14.07.2022 | canceled |
| 21.07.2022 | Open Discussion How to give a great project presentation Q'n'A: Exam preparation Individual Help for Projects |
| 28.07.2022 | Final Presentation - Room: Pettenkoferstr. 14, Gr. HS Physiologie (F1.02) |
Exercises
| Date | Topic |
|---|---|
| 06.05.2022 | Organization Exercise 01: Recording your own data (2 weeks) |
| 13.05.2022 | Live Coding Session: Getting Started with Neuronal Networks |
| 20.05.2022 | Live Coding Session: Deploying Models to Mobile Devices (Android) Exercise 02: Clearing your data and training the first model (2 weeks) |
| 27.05.2022 | canceled |
| 03.06.2022 | Project Ideation Exercise 03: Training an improved model based on a large dataset (1 week) |
| 10.06.2022 | Project Pitches: Show Current Project Status |
| 17.06.2022 | Individual Help for Projects |
| 24.06.2022 | Individual Help for Projects |
| 01.07.2022 | Individual Help for Projects |
| 08.07.2022 | Individual Help for Projects |
| 15.07.2022 | canceled |
| 22.07.2022 | canceled |
| 29.07.2022 | Final Presentation |
Exam
The exam will consist of two parts:
- Your practical project including the final presentation (1/2 of the final grade)
- An oral online exam of 10 minutes 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 oral online exams will probably take place on 10.08.22, 11.08.22, and 22.09.22.
- The final presentation of the practical projects will take place on 28.07.2022 and 29.07.2022 during the tutorial and lecture times.
- Please register for the exam via Uni2work.
Disclaimer
While LMU is closed, most teaching happens currently online. As teachers, we ask you to be forgiving if things should not work perfectly right away, and we hope for your constructive participation. In this situation, we would also like to explicitly point out some rules, which would be self-evident in real life:- In live meetings, we ask you to responsibly deal with audio (off by default) and bandwidth (video as needed).
- Recording or redirecting streams by participants is not allowed.
- Distributing content (video, audio, images, PDFs, etc.) in other channels than those foreseen by the author is not allowed.
