VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
Machine Perception
Last Updated: 2026-02-05 16:07:52
Abstract
Recent developments in neural networks (aka “deep learning”) 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
We will focus on teaching: how to set up the problem of machine perception, the learning algorithms, network architectures, and advanced deep learning concepts in particular probabilistic deep learning models The course covers the following main areas: I) Foundations of deep learning. II) Advanced topics like probabilistic generative modeling of data (latent variable models, generative adversarial networks, auto-regressive models, invertible neural networks). III) Deep learning in computer vision, human-computer interaction, and robotics. Specific topics include: I) Introduction to Deep Learning: a) Neural Networks and training (i.e., backpropagation) b) Feedforward Networks c) Timeseries modelling (RNN, GRU, LSTM) d) Convolutional Neural Networks for classification II) Advanced topics: a) Latent variable models (VAEs) b) Generative adversarial networks (GANs) c) Autoregressive models (PixelCNN, PixelRNN, TCNs) d) Invertible Neural Networks / Normalizing Flows III) Applications in machine perception and computer vision: a) Fully Convolutional architectures for dense per-pixel tasks (i.e., instance segmentation) b) Pose estimation and other tasks involving human activity c) Neural shape modeling (implicit surfaces, neural radiance fields) d) Closed-loop control and deep reinforcement learning
Resources
Literature
Deep Learning Book by Ian Goodfellow and Yoshua Bengio
Learning Materials (Links)
- Main link
- Information
General Information
- Language
- English
- Levels
- MSC , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- end-of-semester examination
- Mode
- written 180 minutes
- Aids
- limited aids (2 x A4 pages of hand written or digital notes with minimum 11pt font size)
Registration & Places
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Machine Perception |
|
3 h weekly |
| exercise | Machine Perception |
|
2 h weekly |
| independent project | Machine Perception | No time listed | 2 h weekly |
Offered In
-
-
-
-
Application Area (Only necessary and eligible for the Master degree in Applied Mathematics. One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area.)
-
-
-
-
-
-
-
-
-