Artificial Intelligence for Robotics
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.
The students are expected to be familiar with the following material:
- Familiarity with different aspects of probability and statistics (e.g. by having taken courses like Recursive Estimation)
- Basic Knowledge of C++ / Python
- Good understanding of linear algebra.
The number of participants is limited to 50. Enrolment was only valid through registration until Sunday, December 18, 2016. Notifications of acceptance were sent out no on Sunday, January 15, 2017.
Lecture Dates and Topics
Further material (such as information on programming exercises and a virtual machine) will be made available through Moodle.
|1||24.02.2017||Introduction||C. Cadena, I.Gilitschenski||-|
|Ex1||24.02.2017||Python Recap||F. Furrer, M. Pfeiffer||-|
|2||03.03.2017||Machine Learning Basics||C. Cadena||-|
|Ex2||03.03.2017||Python Recap II||F. Furrer, M. Pfeiffer||-|
|3||10.03.2017||More on Probabilities||I. Gilitschenski||-|