6 ECTS credits
150 h study time

Offer 1 with catalog number 4004728DNR 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
English
Partnership Agreement
Under agreement for exchange of courses
Faculty
Faculty of Engineering
Department
Electronics and Informatics
Educational team
Ann NOWE (course titular)
Hugues BERSINI
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 and 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
Evolutionary algorithms


 

 

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, http://ai.vub.ac.be/courses/2014-2015/techniques-artificial-intelligence
Handbook (Required) : Machine Learning, Tom Mitchell, McGraw Hill, 9780071154673, 2004
Additional info

Slides are available at http://ai.vub.ac.be/courses/2014-2015/techniques-artificial-intelligence

For the lecture on 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/)
or
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 the most commonly used AI techniques, including learning algorithms. 

The student can correctly trace a learning algorithm on a small data set or environment.
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: 
The student can explain the outcome of a given algorithm when applied in a given setting.

The student 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 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.

This course contributes to the following programme outcomes of the Master in Applied Computer Sciences:

MA_A: Knowledge oriented competence

1. The Master in Engineering Sciences has in-depth knowledge and understanding of exact sciences with the specificity of their application to engineering
3. The Master in Engineering Sciences has in-depth knowledge and understanding of the advanced methods and theories to schematize and model complex problems or processes
4. The Master in Engineering Sciences can reformulate complex engineering problems in order to solve them (simplifying assumptions, reducing complexity)
5. The Master in Engineering Sciences can conceive, plan and execute a research project, based on an analysis of its objectives, existing knowledge and the relevant literature, with attention to innovation and valorization in industry and society
6. The Master in Engineering Sciences can correctly report on research or design results in the form of a technical report or in the form of a scientific paper
7. The Master in Engineering Sciences can present and defend results in a scientifically sound way, using contemporary communication tools, for a national as well as for an international professional or lay audience
11. The Master in Engineering Sciences can think critically about and evaluate projects, systems and processes, particularly when based on incomplete, contradictory and/or redundant information

MA_B:  Attitude

13. The Master in Engineering Sciences has a critical attitude towards one’s own results and those of others

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 open 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% open book exam.
50% project work.

Academic context

This offer is part of the following study plans:
Master in Applied Sciences and Engineering: Applied Computer Science: Standaard traject