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.
Brief introduction to AI (with focus on search spaces and planning)
Learning of concepts (version spaces and decision trees)
Instanced based learning
Evaluation of hypotheses: confidence, bias variance trade off
Computational learning theory
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/)
Handbook The essence of Artificial Intelligence, by Alison Cawsey (http://www.macs.hw.ac.uk/~alison/essence.html) (Chapters 1-4)
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.
The student must be able to devise and sustain arguments in favor of against some choice of (learning-) technique for a given problem.
He/she can motivate the chosen approach to specialist and non specialists.
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
13. The Master in Engineering Sciences has a critical attitude towards one’s own results and those of others
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:
Within the PRAC Practical Assignment category, the following assignments need to be completed:
50% open book exam.
50% project work.
This offer is part of the following study plans:
Master in Applied Sciences and Engineering: Applied Computer Science: Standaard traject