Introduction to AI and machine learning (9002)
  
Coordinating lecturer :Prof. dr. Gustavo ROVELO RUIZ 
  
Co-lecturer :Prof. dr. Jan VAN DEN BUSSCHE 
  
Member of the teaching team :De heer Gilles EERLINGS 
 De heer Jos STEEGMANS 
 De heer Jos THYS 

Language of instruction : English
Credits: 6,0
  
Period: semester 1 (6sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   No sequentiality

Prerequisites

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


Content

This course introduces the basics of artificial intelligence. In particular, it focuses on concepts and applications of Machine Learning as an essential tool for a computer scientist.

Topics covered include the following:

  • an introduction to the broad field of Artificial Intelligence;
  • supervised and unsupervised learning algorithms;
  • a machine learning pipeline (from data collection to model evaluation);
  • realisation of practical implementations based on programming examples using modern software libraries and ML tools;
  • search spaces and gradient descent algorithms;
  • an introduction to Natural Language Processing and Large Language Models;
  • SAT solving and SMT solving and their relationship with AI applications (formal methods, logic and automated reasoning).

The theory is put into practice through several programming assignments in which the studied algorithms have to be implemented and their implications discussed with the teaching team. Active participation of the student is thus expected.


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Small group session ✔  


Evaluation

Semester 1 (6,00sp)

Evaluation method
Other evaluation method during teaching period30 %
Other Written exercises during work sessions, with oral explanation.
Written exam70 %
Closed-book ✔

Second examination period

Evaluation second examination opportunity different from first examination opprt
Yes
Explanation (English)The retake exam consists of a written exam for 100% of the points.
 

Compulsory course material
 

Slides from the lectures.
Other necessary study materials will be made available via Blackboard.


Learning outcomes
Bachelor of Computer Science
  •  EC 

    • The Bachelor of Computer Science graduate has a broad frame of reference that allows him/her to continually update his/her own knowledge and skills in the area of computer science. 

     
  •  DC 

    • De student heeft inzicht in andere disciplines waarop informatica wordt toegepast of die een toepassing vinden in de informatica.

  •  EC 

    • The Bachelor of Computer Science graduate is aware of the ethical-social context in which computer science is used. He/she can recognize and analyze ethical and deontological problems, and act accordingly.

     
  •  DC 

    • De student kan het belang van integriteit uitleggen en daarnaar handelen.

     
  •  DC 

    • De student kan maatschappelijke aspecten en uitdagingen gerelateerd aan informatica uitleggen.

  •  EC 

    • The Bachelor of Computer Science graduate can model and analyse a real-life computer problem, use the own creativity to solve partial problems and combine the solutions found to solve the original problem.

     
  •  DC 

    • De student kan een informaticaprobleem analyseren door het op te splitsen in meer beheersbare deelproblemen.

     
  •  DC 

    • De student kan een probleem uit de praktijk als informaticaprobleem modelleren.

  •  EC 

    • The Bachelor of Computer Science graduate can work in a team on a project of moderate complexity. In this respect, not only domain knowledge aspects are important, but also communication and social skills and the ability to make good task agreements.

     
  •  DC 

    • De student kan constructief samenwerken aan een opdracht of project.

     
  •  DC 

    • De student kan reflecteren op het eigen functioneren in een groepswerk en, indien nodig, het eigen handelen bijsturen.

     
  •  DC 

    • De student kan reflecteren op het functioneren van de groepsleden binnen de samenwerking.

  •  EC 

    • The Bachelor of Computer Science graduate can compare and weigh options for solving a computer science problem and the tools available to do so, based on their usefulness, correctness, and efficiency.

     
  •  DC 

    • De student kan een oplossing voor een probleem toepassen.

     
  •  DC 

    • De student kan geschikte technologie voor de implementatie van een oplossing selecteren en gebruiken.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
3rd year Bachelor of Computer Science J
Bridging Programme Master of Computer Science 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.