Universität Wien FIND
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052414 VU Concepts and Models of Knowledge Engineering (2016W)

Continuous assessment of course work

Summary

1 Karagiannis, Moodle
2 Karagiannis, Moodle

Registration/Deregistration

Groups

Group 1

max. 25 participants
Language: English
LMS: Moodle

Lecturers

Classes (iCal) - next class is marked with N

Wednesday 05.10. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
Wednesday 12.10. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
Wednesday 19.10. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
Wednesday 09.11. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
Wednesday 16.11. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
Wednesday 23.11. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
Wednesday 30.11. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
Wednesday 07.12. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
Wednesday 14.12. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
Wednesday 11.01. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
Wednesday 18.01. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
Wednesday 25.01. 08:00 - 09:30 Hörsaal 1, Währinger Straße 29 1.UG
Wednesday 25.01. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG

Assessment and permitted materials

At the end of the semester, a written test has to be passed. The test will contain theory questions and applied problems. During the test, no unauthorized materials are allowed and all electronic devices have to be turned off.

Until the end of the Semester, and a project has to be completed. The project addresses the topics "Factory of the Future", "Cyber-Physical Systems" and "Internet of Things". Thereby, a use case for robotic arms is created in a design phase and a a solution is implemented in a development phase. Solutions have to be independent for each student.

During the semester, homework assignments have to be submitted. Solutions have to be independent for each student.

In detail, the grade is constituted by:
* Written test 50 %
* Project 35%
* 6 Homework assignments (15%)

Missing class more than three times results in a negative grade

Group 2

max. 25 participants
Language: English
LMS: Moodle

Lecturers

Classes (iCal) - next class is marked with N

Wednesday 05.10. 08:00 - 09:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 12.10. 08:00 - 09:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 19.10. 08:00 - 09:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 09.11. 08:00 - 09:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 16.11. 08:00 - 09:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 23.11. 08:00 - 09:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 30.11. 08:00 - 09:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 07.12. 08:00 - 09:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 14.12. 08:00 - 09:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 11.01. 08:00 - 09:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 18.01. 08:00 - 09:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 25.01. 08:00 - 09:30 Hörsaal 1, Währinger Straße 29 1.UG
Hörsaal 3, Währinger Straße 29 3.OG

Assessment and permitted materials

At the end of the semester, a written test has to be passed. The test will contain theory questions and applied problems. During the test, no unauthorized materials are allowed and all electronic devices have to be turned off.

Until the end of the Semester, and a project has to be completed. The project addresses the topics "Factory of the Future", "Cyber-Physical Systems" and "Internet of Things". Thereby, a use case for robotic arms is created in a design phase and a a solution is implemented in a development phase. Solutions have to be independent for each student.

During the semester, homework assignments have to be submitted. Solutions have to be independent for each student.

In detail, the grade is constituted by:

* Written test 50 %
* Project 35%
* 6 Homework assignments (15%)

Missing class more than three times results in a negative grade

Information

Aims, contents and method of the course

In this lecture, the goal is to gain expertise on the state of the art in knowledge engineering. Therefore, students explore the relevant theory and reinforce their knowledge in exercises. Afterwards, knowledge engineering skills are applied on the topics "Factory of the Future", "Cyber-Physical Systems" and "Internet of Things".

Minimum requirements and assessment criteria

To receive a positive grade, 50 out of 100 percent are required. Grades are as follows:

* sehr gut (1) >= 87,00%
* gut (2) >= 75,00%
* befriedigend (3) >= 62,00%
* genügend (4) >= 50,00 %
* nicht genügend (5) < 50,00 %

Examination topics

Agent Systems
Neural Networks I
Machine Learning
Neural Networks II
Constraint Satisfaction
Evolutionary Computation I
Evolutionary Computation II
Hidden Markov Models
Semantic Web

Reading list

Script with lecture content
Moodle course

Dimitris Karagiannis, Rainer Telesko (2001), Wissensmanagement: Konzepte der künstlichen Intelligenz und des Softcomputing
Stuart J. Russell, Peter Norvig (2009), Artificial Intelligence - A Modern Approach

Association in the course directory

Last modified: Fr 31.08.2018 08:48