151-0664-00L  Artificial Intelligence for Robotics

SemesterSpring Semester 2017
LecturersI. Gilitschenski, C. D. Cadena Lerma, R. Siegwart
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
151-0664-00 VArtificial Intelligence for Robotics2 hrs
Fri08:15-10:00CAB G 61 »
I. Gilitschenski, C. D. Cadena Lerma, R. Siegwart
151-0664-00 UArtificial Intelligence for Robotics2 hrs
Fri10:15-12:00CAB G 61 »
I. Gilitschenski, C. D. Cadena Lerma, R. Siegwart

Catalogue data

AbstractThis 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.
ObjectiveWorking knowledge of basic methods from statistics and machine learning.
ContentProbability Recap; Basic Concepts of Machine Learning; Regression; Dimensionality Reduction; Clustering; Support Vector Machines; Deep Learning;
Lecture notesAll relevant materials will be made available through the website of the course.
LiteratureWill be announced in the first lecture.
Prerequisites / NoticeThe 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)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersR. Siegwart, C. D. Cadena Lerma, I. Gilitschenski
Typeend-of-semester examination
Language of examinationEnglish
RepetitionA repetition date will be offered in the first two weeks of the semester immediately consecutive.
Additional information on mode of examinationThe 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.

Learning materials

 
Main linkASL lectures
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Mechanical Engineering MasterRobotics, Systems and ControlWInformation
Robotics, Systems and Control MasterCore CoursesWInformation