6 ECTS credits
150 h study time

Offer 1 with catalog number 4019822DNR for all students in the 2nd semester at a (D) Master - preliminary level.

Semester
2nd semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Taught in
Dutch
Partnership Agreement
Under agreement for exchange of courses
Faculty
Faculteit Ingenieurswetenschappen
Department
Electronics and Informatics
Educational team
Lesley De Cruz (course titular)
Activities and contact hours
36 contact hours Lecture
24 contact hours Seminar, Exercises or Practicals
Course Content

Brief introduction to AI( with focus on search spaces, expert systems, planning)
Learning of concepts (version spaces and decision trees)
Instanced based learning
Reinforcement Learning
Bayesian Learning
Neural Networks
Evaluation of hypotheses: confidence, bias variance trade off
Computational learning theory

Course material
Digital course material (Required) : slides, http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html en http://como.vub.ac.be/teaching.html
Handbook (Required) : Machine Learning, Tom Mitchell, BIB, 9780071154673, 2004
Additional info

 


Slides are available at http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html, + extra slides at http://como.vub.ac.be/teaching.html

Part state space search, there are many good introductory books, for example:

Handbook Artificial Intelligence: A Modern Approach, by Russel and Norvig (http://aima.cs.berkeley.edu/)

 

Handbook The essence of Artificial Intelligence, by Alison Cawsey (http://www.macs.hw.ac.uk/~alison/essence.html) (Chapters 1-4)

 

Learning Outcomes

Algemene competenties

Knowlegde and insight
The student is acquainted with a range of AI techniques, including learning algorithms.
The student is capable of applying evaluation techniques to estimate the performance of the obtained results and to calculate the performance of an algorithm given a concrete application context.

The use of knowledge and insight:
He is able to choose the appropriate techniques given a concrete learning problem, to apply them correctly and to evaluate the obtained results.

Judgement ability
The student must be able to to devise and sustain arguments in favor of against some choice of (learning-) technique for a given problem.


Communication
He/she can motivate the chosen approach to specialist and non specialists.

Skills
Students have obtained the skills to autonomously develop, program, analyse, and apply learning techniques to a wide variety of problems.

Grading

The final grade is composed based on the following categories:
Written Exam determines 50% of the final mark.
PRAC Practical Assignment determines 50% of the final mark.

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

  • written closed book exam with a relative weight of 1 which comprises 50% of the final mark.

    Note: 50% open book exam.

Within the PRAC Practical Assignment category, the following assignments need to be completed:

  • project work with a relative weight of 1 which comprises 50% of the final mark.

    Note: 50% project work.

Additional info regarding evaluation

50% closed book exam.
50% project work.

 
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:
Master of Photonics Engineering: Standaard traject (only offered in Dutch)
Master of Applied Sciences and Engineering: Applied Computer Science: Standaard traject (only offered in Dutch)
Master of Teaching in Science and Technology: ingenieurswetenschappen (120 ECTS, Etterbeek) (only offered in Dutch)