Search result: Catalogue data in Autumn Semester 2020
|Computer Science Master|
|Master Studies (Programme Regulations 2020)|
|Minor in Computer Vision|
|263-3210-00L||Deep Learning||W||8 credits||3V + 2U + 2A||T. Hofmann|
|Abstract||Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.|
|Objective||In 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 / Notice||This 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
Computational Intelligence Lab
Introduction to Machine Learning
Statistical Learning Theory
Probabilistic Artificial Intelligence
|263-5902-00L||Computer Vision||W||8 credits||3V + 1U + 3A||M. Pollefeys, S. Tang, V. Ferrari|
|Abstract||The 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.|
|Objective||The 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.
|Content||Camera 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 / Notice||It is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course.|
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