5 ECTS credits
150 h study time

Offer 2 with catalog number 2019977BNR for all students in the 2nd semester at a (B) Bachelor - advanced level.

Semester
2nd semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Enrollment Requirements
.
Taught in
English
Faculty
Faculty of Sciences and Bioengineering Sciences
Department
Computer Science
Educational team
Geraint Wiggins (course titular)
Activities and contact hours
26 contact hours Lecture
26 contact hours Seminar, Exercises or Practicals
125 contact hours Independent or External Form of Study
Course Content

This course presents a broad and deep coverage of the fundamental concepts of a range of approaches to Artificial Intelligence.

1.    Introduction to Artificial Intelligence
2.    Intelligent Agents
3.    Knowledge Representation and Reasoning
4.    Searching for Solutions
5.    Learning to classify
6.    Representing and reasoning with uncertainty

Additional info

Supplementary information like the course slides, AI Resources on the Web, an Online Code Repository in Python and Comments and Discussion can be found on the webpage of the book used in the course, i.e. http://aima.cs.berkeley.edu/.

Learning Outcomes

Algemene competenties

•    Knowledge and insight: On passing this course, a student will have a good overview and basic knowledge of symbolic AI-techniques, formalisms and their applications.
•    Application of knowledge and insight: On passing this course, a student will be able to make judgements about when particular techniques should be applied to solve particular problems and to compare and contrast the advantages of different techniques for particular problems.
•    Independent thinking: On passing this course, a student will be equipped to collect and interpret literature about topics in AI. He or she will be able to understand basic and intermediate literature to a sufficient level to implement AI solutions to given problems.
•    Communication: On passing this course, a student will be able to communicate ideas about symbolic AI to experts and non-experts.
•    Skills: On passing this course, a student will have further developed independent learning skills and autonomy in scientific and engineering work.

 

Grading

The final grade is composed based on the following categories:
Other Exam determines 100% of the final mark.

Within the Other Exam category, the following assignments need to be completed:

  • Examen with a relative weight of 1 which comprises 100% of the final mark.

Additional info regarding evaluation

Written exam.

Allowed unsatisfactory mark
The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context

This offer is part of the following study plans:
Bridging Programme Master of Science in Applied Sciences and Engineering: Computer Science: Standaard traject (only offered in Dutch)
Bridging Programme Master of Science in Applied Computer Science: Standaard traject (only offered in Dutch)
Preparatory Programme Master of Science in Applied Sciences and Engineering: Computer Science: Track C (Ind Ing, 61 ECTS) (only offered in Dutch)
Preparatory Programme Master of Science in Applied Sciences and Engineering: Computer Science: Track A (76 ECTS) (only offered in Dutch)
Preparatory Programme Master of Science in Applied Sciences and Engineering: Computer Science: Track B (65 ECTS) (only offered in Dutch)
Preparatory Programme Master of Science in Applied Computer Science: Enkel voor studenten industriële wetenschappen (only offered in Dutch)
Preparatory Programme Master of Science in Applied Computer Science: Track A (58 ECTS) (only offered in Dutch)
Preparatory Programme Master of Science in Applied Computer Science: Track B (52 ECTS) (only offered in Dutch)