263-3710-00L Machine Perception
|Semester||Spring Semester 2021|
|Lecturers||O. Hilliges, S. Tang|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|Comment||Number of participants limited to 200.|
|263-3710-00 V||Machine Perception||3 hrs|
|O. Hilliges, S. Tang|
|263-3710-00 U||Machine Perception||2 hrs|
|O. Hilliges, S. Tang|
|263-3710-00 A||Machine Perception||2 hrs||O. Hilliges, S. Tang|
|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 intelligent UIs. This course is a deep dive into deep learning algorithms and architectures with applications to a variety of perceptual tasks.|
|Objective||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.
|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) 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
Book by Ian Goodfellow and Yoshua Bengio
|Prerequisites / Notice||*** |
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.
|Performance assessment information (valid until the course unit is held again)|
|Performance assessment as a semester course|
|ECTS credits||8 credits|
|Examiners||O. Hilliges, S. Tang|
|Language of examination||English|
|Repetition||The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.|
|Mode of examination||written 120 minutes|
|Additional information on mode of examination||The grade of the course is determined by mandatory project work (40%) and the final written exam (60%).|
|Written aids||limited aids (2 x A4 pages of hand written notes)|
|This information can be updated until the beginning of the semester; information on the examination timetable is binding.|
|Only public learning materials are listed.|
|No information on groups available.|
|Places||200 at the most|
|Priority||Registration for the course unit is until 07.03.2021 only possible for the primary target group|
|Primary target group||Cyber Security MSc (260000)
Cyber Security MSc (EPFL) (260100)
Data Science MSc (261000)
Computer Science MSc (263000)
CAS ETH in Computer Science (269000)
Computer Science (Mobility) (274000)
|Waiting list||until 19.03.2021|