This course provides tools from statistics and machine learning enabling the participants to deploy them as part of typical perception pipelines. All methods provided within the course will be discussed in context of and motivated by example applications from robotics. The accompanying exercises will involve implementations and evaluations using typical robotic datasets.
Working knowledge of basic methods from statistics and machine learning.
Probability Recap; Basic Concepts of Machine Learning; Regression; Dimensionality Reduction; Clustering; Support Vector Machines; Deep Learning;
All relevant materials will be made available through the website of the course.
Will be announced in the first lecture.
Prerequisites / Notice
The students are expected to be familiar with the following material: Lecture on Recursive Estimation / Basic Knowledge of C++ / Good understanding of elementary probability and linear algebra. The number of participants is limited to 50. Enrolment is only valid through registration on the ASL website (www.asl.ethz.ch) and will open on 12 December 2016.