Vorlesung: Machine Learning in Graphics & Vision

Content

This new course will cover machine learning algorithms as well as their application to computer vision and computer graphics problems.

 

Machine Learning Topics:

  • Classification
  • Regression
  • Random Forests
  • Deep Neural Networks
  • Generative Models
  • Generative Adversarial Networks
  • Structured Prediction
  • MRF / CRF

Vision and Graphics Applications:

  • Semantic Segmentation
  • Optical Flow
  • Structure from Motion
  • Video Deblurring
  • Rendering Faces
  • Global Illumination Sampling

Overview

  • SWS: 2 V + 2 Ü
  • 6 ECTS
  • Veranstaltungsnummer: INF

Lectures
Thursdays 8–10, Room F122 first lecture on 19. April 2018

Exercise groups
Fridays 8-10, Room F122 first exercise meeting on 20. April 2018

News

Exercises

By continuous and active participation in the weekly exercises, students may obtain a 0.3 bonus on the final grade, when passing the exam. To qualify for this bonus, the student must successfully solve 60% of the assigned homework problems which will be determined by grading the submitted homework solutions.

 

Homework problems might require coding in Python or C++. Make sure you are familiar with Python. If you have a lot of programming experience but in a different language, you will probably be fine.

 

To be able to login into our machines in the computer pool, you are required to fill out the application for a WSI user account.

Lecture dates

Lectures

  • Thursday, 8-10 h, Room F 122

Exercise groups

  • Friday, 8-10 h, Room F 122

 

Exam Dates

  • Tue, 31.7.2018

  • Tue, 18.9.2018