Machine Learning (incl. Deep Learning) (4174)
  
Coordinating lecturer :Prof. dr. Gustavo ROVELO RUIZ 
  
Member of the teaching team :De heer Gilles EERLINGS 
 De heer Jarne THYS 

Language of instruction : English
Credits: 6,0
  
Period: semester 2 (6sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   No sequentiality
   Mandatory sequentiality bound on the level of programme components
 
 
  Following programme components must have been included in your study programme in a previous education period
    Probability theory and statistics (2941) 6.0 stptn
 

Prerequisites

The student can program fluently.
The studen has basic knowledge of statistics and linear algebra.


Content

The course gives an overview of different techniques within machine learning: methods for regression, classification including neural networks, and reinforcement learning.

Students acquire basic knowledge about the theoretical background of the techniques and are able to apply them practically in mini-projects where they can explain and present the acquired results.


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Practical ✔  
Project ✔  
Self-study assignment ✔  
Teaching methods  
Exercises ✔  
Presentation ✔  
Report ✔  


Evaluation

Semester 2 (6,00sp)

Evaluation method
Oral evaluation during teaching period20 %
Open questions✔
Presentation✔
Other evaluation method during teaching period30 %
Other:Project
Oral exam50 %
Open questions✔
Evaluation conditions (participation and/or pass)✔
Conditions

The student has to participate in all parts of the evaluation. 
Consequences

If the condition is not met, no final grade will be awarded but an ‘N’.
Additional information

The oral evaluation during the education period is related to the analysis of a scientific paper. 

The practical evaluation during the education period consists of a project where the student will have to develop an ML system and write a report motivating design decisions.

For the retake exam, students must retake only the part for which they failed. If this concerns the evaluation during the semester, the student will have to complete both the assignments, the practical evaluation and the oral evaluation. 

 

 

 

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory course material
 

Course slides and copies of articles. 

The course material is distributed during the class or made available via the web (Blackboard) . 


Learning outcomes
Master of Computer Science
  •  EC 

    • EC 1: A graduate of the Master of Computer Science programme has insight into the most important technological developments in the field of computer science and the underlying scientific principles.

  •  EC 

    • EC 5: A graduate of the Master of Computer Science programme is able to independently model a complex problem in computer science, to introduce the necessary abstractions, to describe and to implement the solution in a structured manner, and, finally, to discuss with the stakeholders why the chosen solution and the corresponding implementation meet with the specifications.

  •  EC 

    • EC 6: A graduate of the Master of Computer Science programme is able to independently situate a scientific problem, analyse and evaluate it, to formulate a research question and propose a solution for this in a scientifically substantiated manner.

  •  EC 

    • EC 8: A graduate of the Master of Computer Science programme is able to communicate information, ideas and solutions to an audience of fellow computer scientists and to non-specialists by expressing him or herself on the proper level of abstraction.

  •  EC 

    • EC 9: A graduate of the Master of Computer Science programme is able to clearly report both orally and verbally on his or her work in a national and international context.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
exchange bachelor informatica K J
exchange master informatica K J
Master Computer Science profile Artificial Intelligence J
Master of Computer Science choice J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.