Faculty of Sciences

Master's degree in Intelligent Systems Engineering

Formation's goal

Target profiles and skills

The discipline of Intelligent Systems Engineering has experienced a rapid evolution in recent years in the world. Master's programs in artificial intelligence and intelligent systems are offered in most universities around the world. It was only a matter of time before modern artificial intelligence was integrated into graduation curricula. In today's society, artificial intelligence and machine learning are becoming more and more prevalent. With the advent of the web, millions of people are already familiar with software integrating artificial intelligence such as web search, e-commerce, gaming sites. Many artificial intelligence techniques are used in bioinformatics and chemoinformatics, computer security: spam filtering, modern computer games and robotics. Medical informatics and knowledge-based systems have already penetrated hospitals. Computer imaging is already used in surveillance systems in the field of computer security. Finally, let's not forget the ICT industry, which was born thanks in large part to the competition of artificial intelligence.

Algerian business has fallen far behind in these different disciplines and the university must play an avant-garde role in this field.

The specific objectives of this training is to supplement the teaching provided by the already operational masters with courses not yet available in the computer science department so that all the masters can cover all the themes of this vast discipline that is computer science. . This Master's offer can be considered as a single degree that does not distinguish between the Academic type and the Professional type.

The proposed training allows students above all to acquire advanced knowledge in the field of computer science but in addition to approach complex problems with intelligent techniques of topicality. This training is necessary to understand real business problems because most of the problems encountered in business are complex and require a significant mastery of powerful techniques to solve them. Artificial intelligence inherently offers intelligent approaches to problem solving. The winners of this training will then be able to choose freely and move towards the specialty they want such as computer security, medical informatics, e-commerce, web research, web services , in the ICT industry, in documentary computing, etc.

 

Master's program in Intelligent Systems Engineering (AI)

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1st year

1 semester

 

Teaching unit

VHS weekly VH  

coefficient

 

Credits

Assessment method
14-16 weeks C TD TP T.personal Continued Examen
UE Fundamentals 18
UEF1.1 : 18
Advanced algorithms 67:30 pm 1:30 pm 1:30 pm 1:30 pm 1:30 pm 3 6 40% 60%
Artificial intelligence 67:30 pm 1:30 pm 1:30 pm 1:30 pm 1:30 pm 3 6 40% 60%
Meta-heuristics and Evolutionary Algorithms 67:30 pm 1:30 pm 1:30 pm 1:30 pm 1:30 pm 3 6 40% 60%
EU Methodology 9
EMU1.1 :
Advanced Computer Architecture 45H 1:30 pm 1:30 pm 1:30 pm 2 4 40% 60%
Representation of Knowledge

and reasoning1

45H 1:30 pm 1:30 pm 1:30 pm 2 3 40% 60%
Data analysis 22:30 pm 1:30 pm 1H 2 2 100%
Discovery Teaching Unit 2
UED1.1:
Mathematics for AI 45:00 pm 1:30 pm 1:30 pm 1H 1 2 40% 60%
transversal teaching units 1
UET1.1:
Data visualization 22:30 pm 1:30 pm 1H 1 1 100%
Total Semester 1 382:30 pm 12H 9:00 pm 4:30 pm 10:30 pm 17 30

 

 

2 semester

Teaching unit VHS weekly VH coefficient Credits Assessment method
14-16

PULL

C TD TP Personal T. Accounts

nu

Examination

n

UE Fundamentals     18    
UEF2.1:             18    
Neural Networks and Machine Learning  

67:30 pm

 

1:30 pm

 

1:30 pm

 

1:30 pm

 

1:30 pm

 

3

 

6

40% 60%
Representation and reasoning of knowledge 2  

67:30 pm

 

1:30 pm

 

1:30 pm

 

1:30 pm

 

1:30 pm

 

3

 

6

40% 60%
Automatic language processing  

67:30 pm

 

1:30 pm

 

1:30 pm

1:30 pm  

1:30 pm

 

3

 

6

40% 60%
EU Methodology             9    
EMU2.1:                  
data mining 45H 1:30 pm 1:30 pm   1:30 pm 2 4 40% 60%
Advanced Databases 45H 1:30 pm   1:30 pm 1:30 pm 2 3 40% 60%
Lyric processing 22:30 pm 1:30 pm     1H 2 2   100%
Discovery Teaching Unit     2    
UED2.1:                  
Networks 45H 1:30 pm   1:30 pm 1H 1 2 40% 60%
Transversal UE     1    
UET2.1:                  
Entrepreneurship 22:30 pm 1:30 pm     1H 1 1   100%
Total Semester 2 382:30 pm 12H 6H 7:30 pm 10:30 pm 17 30    

 

 

 

2nd year

3 semester

Unit

of Teaching

VHS weekly VH  

coefficient

 

Credits

Assessment method
14-16

PULL

C TD TP T.staff Continued Examen
UE Fundamentals 18
UEF3.1: 18
Advanced Machine Learning 67:30 pm 1:30 pm 3:00 pm 1:30 pm 3 6 40% 60%
Data Warehouse and Big Data 67:30 pm 1:30 pm 1:30 pm 1:30 pm 1:30 pm 3 6 40% 60%
Agent Technology 67:30 pm 1:30 pm 1:30 pm 1:30 pm 1:30 pm 3 6 40% 60%
EU Methodology
EMU3.1: 9
Image processing 45H 1:30 pm 1:30 pm 1:30 pm 2 4 40% 60%
Artificial Vision 45H 1:30 pm 1:30 pm 1:30 pm 2 3 40% 60%
Ontologies and web

semantics

22:30 pm 1:30 pm 1H 2 2 100%
Discovery Teaching Unit
UED3.1: 2
Computer Systems Security 45H 1:30 pm 1:30 pm 1H 1 2 40% 60%
Transversal UE
UET3.1: 1
Writing dissertations and scientific articles 22 H 30 1:30 pm 1H 1 1 100%
Total Semester 3 382:30 pm 12:00 pm 4:30 pm 9:00 pm 10:30 pm 17 30

4 semester

  • Overall summary of the training: (indicate the separate global VH in progress, TD, for the 04 teaching semesters, for the different types of UE)

 

UE

VH

UEF UEM UED UET Total
Courses 202,5 202,5 67,5 67,5 540
TD 180 67,5 45   292,5
TP 225 67,5 22,5   315
Personal work 202,5 180 45 45 472,5
Other (internship)         280H
Other (seminar)         21H
Total 810 517,5 180 112,5  
Credits 84 27 6 3 120
% in credits for each teaching unit 70% 22,5% 5% 2.5% 100%