Department for Informatics | Sitemap | LMU-Portal
Deutsch
  • Home
  • Future Students
  • Enrolled students
  • Teaching
    • Archive
    • SS 2023
      • CC
      • CG1
      • DS
      • DW2
      • HCA
      • HS
      • MMI1
      • MMP
      • MT
      • PAR
      • PEM_RL
      • PGD
      • PKMM
      • PML
      • PS
      • PSK
      • PTP
      • PVRU
      • SWH
      • SWH-NEBENFACH
      • USEC
      • UX2
      • UX3
      • WAL
  • Research
  • People
  • Contact
  • Jobs
  • Internal
  • COVID-19 special: online teaching
PML in other semesters:
SS23 SS22 SS21
Home > Teaching > SS 2023 > PML

Lecture Practical Machine Learning

Moodle
Enrollment key: 1234

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
  • News
  • Requirements
  • Syllabus
  • Lectures
  • Exercises
  • Exam

Dates and Locations

  • Lecture:
    Date: Thu, 10-12 c.t.
    Location: Pettenkoferstr. 14, Kl. HS Physiologie (F1.08)
    First session: 20.04.2023
  • Tutorial:
    Date: Fri, 10-12 c.t.
    Location: Pettenkoferstr. 14, Kl. HS Physiologie (F1.08)
    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
27.05.2022 canceled
TBA Lecture 02: Supervised vs. Unsupervised Learning
Lecture 03: Full Practical Neural Network Walkthrough
TBA Lecture 04: Introduction Neural Networks
Lecture 05: Advanced Neural Networks
TBA Lecture 06: Evaluating Neural Networks
Lecture 07: Trainings Strategies
TBA Lecture 08: Recurrent Neural Network (RNN) & Long Short-Term Memory (LSTM)
TBA Lecture 09: Generative Adversarial Networks (GANs)
TBA Lecture 10: Reinforcement Learning
TBA Open Discussion
How to give a great project presentation
Q'n'A: Exam preparation
Individual Help for Projects
TBA Final Presentation - Room: Pettenkoferstr. 14, Gr. HS Physiologie (F1.02)

Exercises

Date Topic
05.05.2022 Organization
Exercise 01: Recording your own data (2 weeks)
TBA Live Coding Session: Getting Started with Neuronal Networks
TBA Live Coding Session: Deploying Models to Mobile Devices (Android)
Exercise 02: Clearing your data and training the first model (2 weeks)
TBA Project Ideation
Exercise 03: Training an improved model based on a large dataset (1 week)
TBA Project Pitches: Show Current Project Status
TBA Individual Help for Projects
TBA Individual Help for Projects
TBA Individual Help for Projects
TBA Individual Help for Projects
TBA 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 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.
To top
Impressum – Privacy policy – Contact  |  Last modified on 2023-03-16 by Jesse Grootjen (rev 41841)