Tools of Artificial intelligence
Academic Study Board of the Faculty of Engineering
Teaching language: English
EKA: T550021102
Censorship: Second examiner: External
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master
Course ID: T550021101
ECTS value: 5
Date of Approval: 30-09-2020
Duration: 1 semester
Version: Archive
Course ID
Course Title
ECTS value
5
Internal Course Code
Responsible study board
Administrative Unit
Date of Approval
Course Responsible
| Name | Department | |
|---|---|---|
| Dirk Kraft | kraft@mmmi.sdu.dk | Mærsk Mc-Kinney Møller Instituttet |
| Kamilla Juel Sørensen | kjs@tek.sdu.dk | TEK Uddannelseskoordinering og -support, Det Tekniske Fakultet |
Teachers
| Name | Department | City | |
|---|---|---|---|
| Poramate Manoonpong | poma@mmmi.sdu.dk | SDU Biorobotics, Mærsk Mc-Kinney Møller Instituttet |
Programme Secretary
| Name | Department | City | |
|---|---|---|---|
| Danny Colmorten | daco@tek.sdu.dk | TEK Uddannelseskoordinering og -support, Det Tekniske Fakultet |
Offered in
Level
Offered in
Duration
Recommended prerequisites
It is recommended that the student has followed the 1st semester course Introduction to Artificial Intelligence (AI1).
Basic object oriented programming knowledge is recommended.
Learning objectives - Knowledge
- understanding of basic machine learning techniques: neural networks, genetic algorithms and reinforcement learning
- understanding of representational techniques appropriate to the above learning methods
- understanding of the experimental challenges and demands of the machine learning approaches referred to above
Learning objectives - Skills
- able to implement, debug and deploy the AI techniques taught in new situations
- able to devise suitable representations of data for chosen machine learning techniques
- able to test, evaluate and document the performance of chosen machine learning techniques using suitable correct methodologies
- able to write a straightforward experimental scientific paper documenting a comparison experiment
Learning objectives - Competences
- can identify robotic problems where machine learning techniques could be applied
- can select appropriate techniques from the toolbox of possibilities
- able to characterise a new AI technique in terms of scope and type (unsupervised, semi-supervised or supervised)
- can evaluate reported applications of machine learning techniques in terms of results and methodology
URL for Skemaplan
Teaching Method
Number of lessons
48 hours per semester
Teaching language
Examination regulations
Exam regulations
Name
Exam regulations
Examination is held
By the end of the semester
Tests
Exam
EKA
T550021102
Name
Exam
Description
Individual written report based on project.
Form of examination
Compulsory assignment
Censorship
Second examiner: External
Grading
7-point grading scale
Language
English
ECTS value
5
Courses offered
| Offer period | Offer type | Profile | Education | Semester |
|---|---|---|---|---|
| Spring 2021 | Mandatory | Drones and Autonomous Systems (DAS) 2020 | Master of Science in Engineering (Robot Systems) | Odense | 2 |
| Spring 2021 | Optional | Embedded Systems 2020 | MSc Electronics, Odense | Master of Science in Engineering (Electronics) | Odense | |
| Spring 2021 | Optional | Power Electronics 2020 | MSc Electronics, Odense | Master of Science in Engineering (Electronics) | Odense | |
| Spring 2021 | Optional | Physics and Technology 2019 | Master of Science in Engineering (Physics and Technology) | Odense | |
| Spring 2021 | Optional | Physics and Technology, 2020 and onwards | Master of Science in Engineering (Physics and Technology) | Odense | |
| Spring 2021 | Mandatory | Advanced Robotics Technology (ART) 2020 | Master of Science in Engineering (Robot Systems) | Odense | 2 |
| Spring 2021 | Optional | Fysik og Teknologi, optag 2018 | Bachelor of Science in Engineering (Physics and Technology) | Odense | |
| Spring 2021 | Optional | Fysik og Teknologi, optag 2019 og frem | Bachelor of Science in Engineering (Physics and Technology) | Odense | |
| Spring 2021 | Optional | Exchangekurser udbudt af TEK - bachelorniveau | Exchange | | Soenderborg, Odense | |
| Spring 2021 | Optional | Exchangekurser udbudt af TEK - kandidatniveau | Exchange | | Soenderborg, Odense | Spring 2021 | Exchange students |