Prof. Dr. Björn Ommer, WS 2016/17
Topic 1: Deep Learning, in particular Deep Convolutional Neuronal Networks (CNNs). As basics for Deep Neural Networks we will discuss convolutions, filters , Fourier analysis, wavelets overcomplete bases, learning, optimization, before going in to detail on neural networks.
Topic 2: Multiview geometry and 3D scene estimation. Subtopics are camera models and camera geometry, stereo, structure from motion, optical flow, depth estimation
The first lecture will be on the 24th of October.
Contents:
The lecture covers two topics:Topic 1: Deep Learning, in particular Deep Convolutional Neuronal Networks (CNNs). As basics for Deep Neural Networks we will discuss convolutions, filters , Fourier analysis, wavelets overcomplete bases, learning, optimization, before going in to detail on neural networks.
Topic 2: Multiview geometry and 3D scene estimation. Subtopics are camera models and camera geometry, stereo, structure from motion, optical flow, depth estimation
The first lecture will be on the 24th of October.
Course Information:
| Extent | Date | Room | Language | |||
|---|---|---|---|---|---|---|
| 2+2 SWS (Lecture, Excersise) | Monday 14.00 - 16.00 | INF 205 / SR A | TBD | |||