Study programme 2021-2022 | Français | ||
Artificial Intelligence and data | |||
Programme component of Master's in Computer Engineering and Management : Specialist Focus on Artificial Intelligence and Decision Aid à la Faculty of Engineering |
Code | Type | Head of UE | Department’s contact details | Teacher(s) |
---|---|---|---|---|
UI-M1-IRIGIA-011-M | Compulsory UE | MAHMOUDI Sidi | F114 - Informatique, Logiciel et Intelligence artificielle |
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|---|---|---|---|---|---|---|---|---|
| Français | 30 | 30 | 0 | 0 | 0 | 5 | 5.00 | 1st term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
---|---|---|---|---|---|---|---|---|
I-MARO-016 | Streaming Data Analysis | 12 | 12 | 0 | 0 | 0 | Q1 | 40.00% |
I-ILIA-026 | Artificial Intelligence | 18 | 18 | 0 | 0 | 0 | Q1 | 60.00% |
Programme component |
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Objectives of Programme's Learning Outcomes
Learning Outcomes of UE
AA "Intelligence Artificielle": at the end of this course, the student will be able to:
- manage, formulate and solve problems in artificial intelligence ;
- master the concepts of artificial intelligence: agents and environment, multi-agent systems, neural networks, machine learning, etc.
- understand the connection between Artificial Intelligence, Data Science, Machine and Deep Learning.
At the end of the class the student will master the fundamentals of the study of univariate time series, which amounts to
- understand and explain a few models in depth;
- get acquainted to the use of a statistical software in view of fitting a model to a time series and using it for forecasting purposes;
- be able to assess the model and the precision of the forecasts.
Content of UE
AA "Intelligence Artificielle":
- Introduction and definition of artificial intelligence;
- Intelligent agents;
- Multi-agent systems;
- Recall and terminology of machine learning;
- Deep neural networks (Deep Learning);
- Types of deep neural networks (MLP, CNN, RNN, etc.).
- Explainable Deep Learning.
AA "Streaming Data Analysis" :
- univariate time series (decomposition: trend, seasonality, cyclic component);
- ARIMA models and Box-Jenkins methodology;
- exponential smoothing;
- overview of multivariate time series.
Prior Experience
Not applicable
Type of Assessment for UE in Q1
Q1 UE Assessment Comments
AA "Intelligence Artificielle": written exam + practical work on computer
AA "Streaming Data Analysis" : written exam + practical work on computer
Type of Assessment for UE in Q3
Q3 UE Assessment Comments
Idem Q1
Type of Resit Assessment for UE in Q1 (BAB1)
Q1 UE Resit Assessment Comments (BAB1)
Not applicable
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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I-MARO-016 |
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I-ILIA-026 |
|
Mode of delivery
AA | Mode of delivery |
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I-MARO-016 |
|
I-ILIA-026 |
|
Required Reading
AA | |
---|---|
I-MARO-016 | |
I-ILIA-026 |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
---|---|
I-MARO-016 | Not applicable |
I-ILIA-026 | Russel, S. Et Norvig, P., (2010) Artificial Intelligence : A Modern Approach 3rd edition, Pearson |
Recommended Reading
AA | |
---|---|
I-MARO-016 | |
I-ILIA-026 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
---|---|
I-MARO-016 | Not applicable |
I-ILIA-026 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
---|---|
I-MARO-016 | C. Chatfield, The analysis of time series, Chapman and Hall, 1989 G. Mélard, Méthodes de prévision à court terme, Editions de l'Université Libre de Bruxelles et Editions Ellipses, 1990 |
I-ILIA-026 | Not applicable |
Grade Deferrals of AAs from one year to the next
AA | Grade Deferrals of AAs from one year to the next |
---|---|
I-MARO-016 | Authorized |
I-ILIA-026 | Unauthorized |