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Otmar Hilliges: Katalogdaten im Frühjahrssemester 2019

NameHerr Prof. Dr. Otmar Hilliges
LehrgebietInformatik
Adresse
Professur für Informatik
ETH Zürich, STD H 24
Stampfenbachstrasse 48
8092 Zürich
SWITZERLAND
Telefon+41 44 632 39 56
E-Mailotmar.hilliges@inf.ethz.ch
URLhttp://people.inf.ethz.ch/otmarh
DepartementInformatik
BeziehungAusserordentlicher Professor

NummerTitelECTSUmfangDozierende
263-3710-00LMachine Perception Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 150.
5 KP2V + 1U + 1AO. Hilliges
KurzbeschreibungRecent 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.
LernzielStudents 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.
InhaltWe 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
LiteraturDeep Learning
Book by Ian Goodfellow and Yoshua Bengio
Voraussetzungen / BesonderesThis 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 150 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 TensorFlow, scikit-learn and scikit-image. We will provide introductions to TensorFlow 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.
263-3712-00LSeminar on Computational Interaction Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 14.

The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
2 KP2SO. Hilliges
KurzbeschreibungComputational Interaction focuses on the use of algorithms to enhance the interaction with a computing system. Papers from scientific venues such as CHI, UIST & SIGGRAPH will be examined in-depth. Student present and discuss the papers to extract techniques and insights that can be applied to software & hardware projects. Topics include user modeling, computational design, and input & output.
LernzielThe goal of the seminar is to familiarize students with exciting new research topics in this important area, but also to teach basic scientific writing and oral presentation skills.
InhaltThe seminar will have a different structure from regular seminars to encourage more discussion and a deeper learning experience. We will use a case-study format where all students read the same paper each week but fulfill different roles and hence prepare with different viewpoints in mind (e.g. "presenter", "historian", "student", etc).

The seminar will cover multiple topics of computational interaction, including:
1) User- and context modeling for UI adaptation
Intent modeling, activity and emotion recognition, and user perception.

2) Computational design
Design mining, design exploration, UI optimization.

3) Computer supported input
Text entry, pointing, gestural input, physiological sensing, eye tracking, and sketching.

4) Computer supported output
Information retrieval, fabrication, mixed reality interfaces, haptics, and gaze contingency

For each topic, a paper will be chosen that represents the state of the art of research or seminal work that inspired and fostered future work. Student will learn how to incorporate computational methods into system that involve software, hardware, and, very importantly, users.

Seminar website: https://ait.ethz.ch/teaching/courses/2019-SS-Seminar-Computational-Interaction/