Search result: Catalogue data in Autumn Semester 2021

Computer Science Master Information
Master Studies (Programme Regulations 2020)
Minors
Minor in Computer Vision
NumberTitleTypeECTSHoursLecturers
263-3210-00LDeep Learning Information Restricted registration - show details
Number of participants limited to 320.
W8 credits3V + 2U + 2AF. Perez Cruz, A. Lucchi
AbstractDeep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.
ObjectiveIn recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. There will also be a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology.
Prerequisites / NoticeThis is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability. The course will make heavy use of mathematics and is not (!) meant to be an extended tutorial of how to train deep networks with tools like Torch or Tensorflow, although that may be a side benefit.

The participation in the course is subject to the following condition:
- Students must have taken the exam in Advanced Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below:

Advanced Machine Learning
Link

Computational Intelligence Lab
Link

Introduction to Machine Learning
Link

Statistical Learning Theory
Link

Computational Statistics
Link

Probabilistic Artificial Intelligence
Link
263-5902-00LComputer Vision Information W8 credits3V + 1U + 3AM. Pollefeys, S. Tang, F. Yu
AbstractThe goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises.
ObjectiveThe objectives of this course are:
1. To introduce the fundamental problems of computer vision.
2. To introduce the main concepts and techniques used to solve those.
3. To enable participants to implement solutions for reasonably complex problems.
4. To enable participants to make sense of the computer vision literature.
ContentCamera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition
Prerequisites / NoticeIt is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course.
263-5905-00LMixed Reality Information W5 credits3G + 1AI. Armeni, F. Bogo, M. Pollefeys
AbstractThe goal of this course is an introduction and hands-on experience on latest mixed reality technology at the cross-section of 3D computer graphics and vision, human machine interaction, as well as gaming technology.
ObjectiveAfter attending this course, students will:
1. Understand the foundations of 3D graphics, Computer Vision, and Human-Machine Interaction
2. Have a clear understanding on how to build mixed reality apps
3. Have a good overview of state-of-the-art Mixed Reality
4. Be able to critically analyze and asses current research in this area.
ContentThe course introduces latest mixed reality technology and provides introductory elements for a number of related fields including:
Introduction to Mixed Reality / Augmented Reality / Virtual Reality Introduction to 3D Computer Graphics, 3D Computer Vision. This will take place in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on mixed reality topics, where small groups of students will work on a particular project with the goal to design, develop and deploy a mixed reality application. The project topics are flexible and can reach from proof-of-concept vision/graphics/HMI research, to apps that support teaching with interactive augmented reality, or game development. The default platform will be Microsoft HoloLens in combination with C# and Unity3D - other platforms are also possible to use, such as tablets and phones.
Prerequisites / NoticePrerequisites include:
- Good programming skills (C# / C++ / Java etc.)
- Computer graphics/vision experience: Students should have taken, at a minimum, Visual Computing. Higher level courses are recommended, such as Introduction to Computer Graphics, 3D Vision, Computer Vision.
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