Module Number INFO-4390 |
Module Title Visual Perception and Learning for Robotics |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Workload:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | - Introduction to robot types and sensors |
|
Objectives | (1) Students can phrase a robotic visual perception problem as algebraic, probabilistic state estimation or machine learning problem and can select an appropriate algorithm for solving it. (2) Students are able to implement a set of robotic visual perception and learning algorithms and analyze their behavior. (3) Students can explain the challenges in robotic visual perception and learning and assess and characterize new methods. |
|
Allocation of credits / grading |
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Lecture
V
o
2
3.0
wt
90
g
100
Tutorial
Ü
o
2
3.0
|
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer / Other | Stückler | |
Literature | Recommended to attend deep learning course before. Basic programming skills in python required. Lecture slides will be provided. Further literature will be announced in the lecture. Recommended textbooks: - An Invitation to 3-D Vision by Yi Ma, Stefano Soatto, Jana Košecká, S. Shankar Sastry - Deep Learning by Goodfellow, Bengio and Courville |
|
Last offered | Wintersemester 2022 | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV |