Machine Learning for Computer Vision: Lecture, Exercise and Project

Computer vision has made tantalizing progress in the past five years, bringing us self-driving cars, automated scene parsing and more. Machine Learning is the driving force behind this revolution.


This course will cover the building blocks of modern computer vision systems, notably

  • unstructured prediction
    • using shallow classifiers such as support vector machines or decision trees
    • using deep classifiers (convolutional neural networks)
  • structured prediction
    • using probabilistic graphical models
  • optimal decisions and policies
    • from reinforcement learning
  • optimization
    • dynamic programming
    • linear and integer linear programming, quadratic programs


As the name suggests, the course consists of 2 hours of lectures per week, practical computer / programming exercises in python, and a project in which you solve a problem of your choice. The latter has a total duration of two to three weeks full-time and can extend into the semester break.

What you need to know beforehand

Some prior exposure to machine learning and / or computer vision are helpful but not strictly needed. You should however be comfortable with all of the following by the start of the course:

  • multivariable calculus, multivariate geometry
  • linear algebra: linear system of equations, eigenproblems
  • probability theory: multivariate random variables, first- and second order moments, Bayes theorem
  • basic python programming

How to join

Just come along for the first lecture on Tuesday, October 17th 2017 at 13:45, but: please be on time. Venue: Mathematikon B, Berliner Str. 43, 3rd floor, seminar room B128.

Lecture notes

Tuesdays, 13:45 to 15:30 in Mathematikon B, Berliner Str. 43, SR B128


Thursdays, 13:00 to 15:00 in Mathematikon B, Berliner Str. 43, SR B128 (same room as the lecture)
Please contact Sven Peter at for questions regarding the exercises.


  • Prince, Simon JD. Computer vision: models, learning, and inference. Cambridge University Press, 2012.

This course will talk about methods which can be used to e.g. segment urban scenes from the CityScape dataset.