263-3710-00L Machine Perception
| Semester | Frühjahrssemester 2021 |
| Dozierende | O. Hilliges, S. Tang |
| Periodizität | jährlich wiederkehrende Veranstaltung |
| Lehrsprache | Englisch |
| Kommentar | Number of participants limited to 200. |
Lehrveranstaltungen
| Nummer | Titel | Umfang | Dozierende | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 263-3710-00 V | Machine Perception | 3 Std. |
| O. Hilliges, S. Tang | ||||||
| 263-3710-00 U | Machine Perception | 2 Std. |
| O. Hilliges, S. Tang | ||||||
| 263-3710-00 A | Machine Perception | 2 Std. | O. Hilliges, S. Tang |
Katalogdaten
| Kurzbeschreibung | 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 intelligent UIs. This course is a deep dive into deep learning algorithms and architectures with applications to a variety of perceptual tasks. |
| Lernziel | Students will learn about fundamental aspects of modern deep learning approaches for perception. 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 HCI. The final project assignment will involve training a complex neural network architecture and applying it on a real-world dataset of human activity. The core competency acquired through this course is a solid foundation in deep-learning algorithms to process and interpret human input into computing systems. 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. |
| Inhalt | 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) Probabilistic deep-learning for generative modelling of data (latent variable models, generative adversarial networks and auto-regressive models). III) Deep learning in computer vision, human-computer interaction and robotics. Specific topics include: I) Deep learning basics: a) Neural Networks and training (i.e., backpropagation) b) Feedforward Networks c) Timeseries modelling (RNN, GRU, LSTM) d) Convolutional Neural Networks for classification II) Probabilistic Deep Learning: a) Latent variable models (VAEs) b) Generative adversarial networks (GANs) c) Autoregressive models (PixelCNN, PixelRNN, TCNs) III) Deep Learning techniques for machine perception: a) Fully Convolutional architectures for dense per-pixel tasks (i.e., instance segmentation) b) Pose estimation and other tasks involving human activity c) Deep reinforcement learning IV) Case studies from research in computer vision, HCI, robotics and signal processing |
| Literatur | Deep Learning Book by Ian Goodfellow and Yoshua Bengio |
| Voraussetzungen / Besonderes | *** In accordance with the ETH Covid-19 master plan the lecture will be fully virtual. Details on the course website. *** This is an advanced grad-level course that requires a background in machine learning. Students are expected to have a solid mathematical foundation, in particular in linear algebra, multivariate calculus, and probability. The course will focus on state-of-the-art research in deep-learning and will not repeat basics of machine learning Please take note of the following conditions: 1) The number of participants is limited to 200 students (MSc and PhDs). 2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge 3) All practical exercises will require basic knowledge of Python and will use libraries such as Pytorch, scikit-learn and scikit-image. We will provide introductions to Pytorch and other libraries that are needed but will not provide introductions to basic programming or Python. The following courses are strongly recommended as prerequisite: * "Visual Computing" or "Computer Vision" The course will be assessed by a final written examination in English. No course materials or electronic devices can be used during the examination. Note that the examination will be based on the contents of the lectures, the associated reading materials and the exercises. |
Leistungskontrolle
| Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
| ECTS Kreditpunkte | 8 KP |
| Prüfende | O. Hilliges, S. Tang |
| Form | Sessionsprüfung |
| Prüfungssprache | Englisch |
| Repetition | Die Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich. |
| Prüfungsmodus | schriftlich 120 Minuten |
| Zusatzinformation zum Prüfungsmodus | The grade of the course is determined by mandatory project work (40%) and the final written exam (60%). |
| Hilfsmittel schriftlich | limited aids (2 x A4 pages of hand written notes) |
| Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan. | |
Lernmaterialien
| Hauptlink | Information |
| Es werden nur die öffentlichen Lernmaterialien aufgeführt. | |
Gruppen
| Keine Informationen zu Gruppen vorhanden. |
Einschränkungen
| Plätze | Maximal 200 |
| Vorrang | Die Belegung der Lerneinheit ist bis 07.03.2021 nur durch die primäre Zielgruppe möglich |
| Primäre Zielgruppe | Cyber Security MSc (260000)
Cyber Security MSc (EPFL) (260100) Data Science MSc (261000) Informatik MSc (263000) CAS ETH in Informatik (269000) Informatik (Mobilität) (274000) |
| Warteliste | Bis 19.03.2021 |
