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)
Download : The Canvas
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% |