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263-3710-00L 8 Credits MSC , WBZ D-ERDW , D-MAVT , D-INFK , D-MATH , D-PHYS , D-ITET
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Machine Perception

Lecturers & Examiners: Prof. em. Dr. Otmar Hilliges, Dr. Manuel Kaufmann, Dr. Xi Wang
VVZ CR 3.67

Last Updated: 2026-06-01 11:33:07

Abstract

Recent developments in neural networks have drastically advanced the performance of machine perception systems in a variety of areas including computer vision, robotics, and human shape modeling.This course is a deep dive into deep learning algorithms and architectures with applications to a variety of perceptual and generative tasks.

Objective

Students will learn about fundamental aspects of modern deep learning approaches for perception and generation. Students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in learning-based computer vision, robotics, and shape modeling. The optional final project assignment will involve training a complex neural network architecture and applying it to a real-world dataset. The core competency acquired through this course is a solid foundation in deep-learning algorithms to process and interpret human-centric signals. In particular, students should be able to develop systems that deal with the problem of recognizing people in images, detecting and describing body parts, inferring their spatial configuration, performing action/gesture recognition from still images or image sequences, also considering multi-modal data, among others.

Content

The courses focuses on teaching how to set up the problem of machine perception and the associated learning algorithms, neural network architectures, and advanced deep learning concepts. The course covers the following main areas: I) Foundations of Deep Learning: Multilayer perceptrons, backpropagation, time-series modeling, convolutional neural networks. II) Advanced topics: latent variable models, generative adversarial networks, auto-regressive models, invertible neural networks, normalizing flows, diffusion models, neural implicit surface representations, neural radiance fields. III) Applications in machine perception and human-centric computer vision: general understanding of human activities, 3D reconstruction of human performance using different input modalities (monocular or multi-view images, body-worn sensors) and representations (explicit triangulated meshes, parametric body models, implicit surfaces, neural radiance fields, 3D Gaussian Splatting-based), Deep Reinforcement Learning and applications in physics-based behavior modeling.

Resources

Literature

Deep Learning Book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Learning Materials (Links)

General Information

Language
English
Levels
MSC , WBZ
Frequency
Yearly recurring

Examination

Type
end-of-semester examination
Mode
written 180 minutes
Aids
No electronic devices and calculators are allowed. A cheat sheet with the following restrictions is allowed: a maximum of 4 DIN A4 pages are allowed. They may be distributed over 2 double-sided sheets of paper or 4 sheets of paper written only on one side. Notes can be written digitally. It is not allowed to paste images or similar into the notes (both physical or digital) and making digital notes excessively small. If digital notes are typeset, the font size must be no smaller than 10 pt.
Optional project work during the semester will earn a bonus of up to 0.25 grade points on top of the final-exam grade. The maximum overall course grade of 6 cannot be exceeded, and can be achieved also without the project work.

Registration & Places

Max Places
300
Priority: Registration for the course unit is until 28.02.2025 only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture Machine Perception
  • Wed 13:15-14:00 (HG F 1)
  • Thu 12:15-14:00 (HG F 1)
3 h weekly
exercise Machine Perception
  • Thu 14:15-16:00 (CAB G 11)
  • Fri 14:15-16:00 (CAB G 11)
2 h weekly
independent project Machine Perception No time listed 2 h weekly

Offered In