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.
Objective
Working knowledge of basic methods from statistics and machine learning.
Content
Probability Recap; Basic Concepts of Machine Learning; Regression; Dimensionality Reduction; Clustering; Support Vector Machines; Deep Learning;
Lecture notes
All relevant materials will be made available through the website of the course.
Literature
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 (Link) and will open on 12 December 2016.
Performance assessment
Performance assessment information (valid until the course unit is held again)
A repetition date will be offered in the first two weeks of the semester immediately consecutive.
Additional information on mode of examination
The final grade is based on an exam at the end of the term. Up to 30% of the grade can be based on the average grade of the quizes, homework, and programming exercises if they are improving the final grade.