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

T550021101

Course Title

Tools of Artificial intelligence

ECTS value

5

Internal Course Code

RMAI2

Responsible study board

Academic Study Board of the Faculty of Engineering

Date of Approval

30-09-2020

Course Responsible

Name Email 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 Email Department City
Poramate Manoonpong poma@mmmi.sdu.dk SDU Biorobotics, Mærsk Mc-Kinney Møller Instituttet

Programme Secretary

Name Email Department City
Danny Colmorten daco@tek.sdu.dk TEK Uddannelseskoordinering og -support, Det Tekniske Fakultet

Offered in

Odense

Level

Master

Offered in

Spring

Duration

1 semester

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

Lectures and exercises

Number of lessons

48 hours per semester

Teaching language

English

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

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