TUM Visual Computing: Prof. Matthias Nießner
The Visual Computing Group offers a variety of lectures and seminars on a regular basis, covering hot areas in computer graphics, vision, and machine learning. Further details are provided on the corresponding lecture websites.

2018 Winter

3D Scanning & Motion Capture (IN2354) 4 SWS, 6 ECTS
DescriptionThis hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt.
ContactProf. Dr. Matthias Nießner
TAs: M.Sc. Aljaž Božič,  M.Sc. Armen Avetisyan
Advanced Deep Learning for Computer Vision (ADL4CV) 5 SWS (2V+3P), 8 ECTS
DescriptionAdvanced Deep Learning. This lecture focuses on cutting edge Deep Learning techniques for computer vision with a heavy focus on Statistical Background, Recurrent Neural Networks (RNNs), and Generative Models (GANs). Part of the lecture is a semester-long project with a deep dive on modern DL methods.
ContactProf. Dr. Matthias Nießner,   Prof. Dr. Laura Leal-Taixé
TAs: M.Sc. Ji Hou,  M.Sc. Andreas Rössler,  M.Sc. Tim Meinhardt
Introduction to Deep Learning (I2DL) (IN2346) 4 SWS, 6 ECTS
DescriptionIntroduction to Deep Learning. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs).
ContactProf. Dr. Laura Leal-Taixé,   Prof. Dr. Matthias Nießner
TAs: M.Sc. Ji Hou,  M.Sc. Andreas Rössler,  M.Sc. Tim Meinhardt
Visual Computing Seminar
DescriptionThis is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning.
ContactDr. Justus Thies,   Prof. Dr. Matthias Nießner

2018 Summer

3D Scanning & Motion Capture
DescriptionThis hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt.
ContactProf. Dr. Matthias Nießner
TAs: M.Sc. Aljaž Božič,  M.Sc. Armen Avetisyan
Introduction to Deep Learning
DescriptionIntroduction to Deep Learning. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs).
ContactProf. Dr. Matthias Nießner,   Prof. Dr. Laura Leal-Taixé
TAs: M.Sc. Ji Hou,  M.Sc. Andreas Rössler,  M.Sc. Tim Meinhardt
Visual Computing Seminar
DescriptionThis is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning.
ContactDr. Justus Thies,   Prof. Dr. Matthias Nießner

2017 Winter

3D Scanning & Motion Capture
DescriptionThis hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt.
ContactProf. Dr. Matthias Nießner
TAs: Dr. Justus Thies,  M.Sc. Aljaž Božič
Deep Learning for Computer Vision
DescriptionIntroduction to Deep Learning for Computer Vision. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs).
ContactProf. Dr. Matthias Nießner,   Prof. Dr. Laura Leal-Taixé
TAs: M.Sc. Thomas Frerix,  M.Sc. Ji Hou,  M.Sc. Andreas Rössler,  M.Sc. Tim Meinhardt
Visual Computing Seminar
DescriptionThis is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning.
ContactProf. Dr. Matthias Nießner

2017 Summer

Deep Learning for Computer Vision
DescriptionIntroduction to Deep Learning for Computer Vision. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs).
ContactProf. Dr. Laura Leal-Taixé,   Prof. Dr. Matthias Nießner
TAs: M.Sc. Thomas Frerix,  M.Sc. Tim Meinhardt
Machine Learning in Graphics
DescriptionIn this course, students will investigate recent research about machine learning techniques in the computer graphics area. Independent investigation for further reading, critical analysis, and evaluation of the topic are required.
ContactProf. Dr. Nils Thuerey,   Prof. Dr. Rüdiger Westermann,   Prof. Dr. Matthias Nießner,   Dr. Kiwon Um