Artificial Intelligence for Robotics

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Short Description

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.

Requirements

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.

# Date Title Lecturer Material
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 -
 
 
Page URL: http://www.asl.ethz.ch/education/lectures/ai_for_robotics.html
25.02.2017
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