- When: Wed 10:15 – 11:45
- Where: schedule
This lecture covers Deep Learning for automatic image and video analysis, such as classifying images into categories or detecting and distinguishing persons. Deep Learning has recently lead to breakthroughs in these fields; in certain problems, the performance of current methods based on this technology is similar or even better than that of humans – a novelty in this field.
The goal of this lecture is to provide a comprehensive introduction to this exciting branch of machine learning with a focus on Convolutional Neural Networks.
You will apply what you’ve learned in the exercise part of this course, which consists of several assignments that must be handed in by each group (two students). You can work on these assignments on your own computer if you have a decent GPU with CUDA support. We will provide remote access to a dedicated GPU server for those who don’t.
This is a course for Master’s students, so students are expected to have basic knowledge of mathematics and statistics, image processing, and machine learning. We will briefly recap some basics as part of the first few lectures.
For the exercise part, students must be able to program in Python 3.
There will be a written exam that covers the lecture part (50% of the grade). The exercise part is also worth 50% of the grade.
- 03.10., 11:00 at EI 3a: Course introduction (slides)
- 10.10., 10:15 at SR Argentinierstraße: Motivation, image classification (slides)
- 17.10., 10:15 at SR Kuppel: Machine learning for image classification (slides)
- 24.10., 10:15 at SR Argentinierstraße: Feature extraction, parametric models
Assignments must be done in groups of two. Groups must be registered via TISS, the deadline is 16.10., 23:00.
- Assignment 1: instructions