263-3710-00L  Machine Perception

Courses

Catalogue data

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.
Learning 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, diffusion models). 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 II) Advanced topics: a) Latent variable models (VAEs) b) Generative adversarial networks (GANs) c) Autoregressive models (PixelCNN, PixelRNN, TCN, Transformer) d) Invertible Neural Networks / Normalizing Flows e) Coordinate-based networks (neural implicit surfaces, NeRF) f) Diffusion models 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) Deep Reinforcement Learning and Applications in Physics-Based Behavior Modeling
Literature	Deep Learning Book by Ian Goodfellow and Yoshua Bengio
Prerequisites / Notice	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 the basics of machine learning Please take note of the following conditions: 1) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge 2) 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 prerequisites: * "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. The exam will be a 3-hour end-of-term exam and take place at the end of the teaching period.
Competencies	Subject-specific Competencies Concepts and Theories assessed Techniques and Technologies assessed Method-specific Competencies Analytical Competencies assessed Problem-solving assessed Project Management assessed Social Competencies Communication fostered Cooperation and Teamwork fostered Leadership and Responsibility fostered Self-presentation and Social Influence fostered Personal Competencies Adaptability and Flexibility fostered Creative Thinking fostered Critical Thinking fostered Integrity and Work Ethics fostered Self-direction and Self-management fostered

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits	8 credits
Examiners	O. Hilliges, J. Song
Type	end-of-semester examination
Language of examination	English
Repetition	The performance assessment is only offered at the end after the course unit. Repetition only possible after re-enrolling.
Mode of examination	written 180 minutes
Additional information on mode of examination	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.
Written aids	limited aids (2 x A4 pages of hand written or digital notes with minimum 11pt font size)

Learning materials

Groups

Restrictions

Offered in

Programme	Section	Type
CAS in Computer Science	Focus Courses and Electives	W
Cyber Security Master	Core Courses	W
Cyber Security Master	Core Courses	W
DAS in Data Science	Image Analysis & Computer Vision	W
DAS in Data Science	Machine Learning and Artificial Intelligence	W
Data Science Master	Core Electives	W
Electrical Engineering and Information Technology Master	Specialization Courses	W
Computer Science Master	Core Courses	W
Computer Science Master	Core Courses	W
Computer Science Master	Minor in Computer Vision	W
Computer Science Master	Minor in Machine Learning	W
Mathematics Master	Machine Learning	W
Robotics, Systems and Control Master	Core Courses	W