 | Study programme 2019-2020 | Français | |
 | Artificial Intelligence | ||
Programme component of Master's in Computer Engineering and Management (Charleroi (Hor. décalé)) à la Faculty of Engineering |
| Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what assessment methods are planned for the end of Q3 |
|---|
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| UI-M1-IRIGIG-846-C | Optional UE | MELOT Hadrien | S825 - Algorithmique |
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| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 30 | 15 | 0 | 0 | 0 | 5 | 5.00 | 2nd term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| S-INFO-061 | Artificial Intelligence | 30 | 15 | 0 | 0 | 0 | Q2 | 100.00% |
| Programme component |
|---|
Objectives of Programme's Learning Outcomes
- Imagine, design, develop, and implement conceptual models and computer solutions to address complex problems including decision-making, optimisation, management and production as part of a business innovation approach by integrating changing needs, contexts and issues (technical, economic, societal, ethical and environmental).
- On the basis of modelling, design a system or a strategy addressing the problem raised; evaluate them in light of various parameters of the specifications.
- Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out computer and management engineering missions, using their expertise and adaptability.
- Master and appropriately mobilise knowledge, models, methods and techniques specific to computer management engineering.
- Analyse and model an innovative IT solution or a business strategy by critically selecting theories and methodological approaches (modelling, optimisation, algorithms, calculations), and taking into account multidisciplinary aspects.
Learning Outcomes of UE
The goal of this course if to initiate the students to classical fields of Artificial Intelligence. The students will be able to identify when a particular method can be applied. The course will concentrate on algorithmic aspect of Artificial Intelligence.
Content of UE
See unique learning activity.
Prior Experience
Knowledge of a programming language (e.g., Python ou Java) and of basic data structures (lists, trees, graphs).
Type of Assessment for UE in Q2
- Written examination
- Graded tests
Q2 UE Assessment Comments
Written exam 85%
Exercices 15%
Type of Assessment for UE in Q3
- Oral examination
Q3 UE Assessment Comments
Oral exam 100%
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| S-INFO-061 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-INFO-061 |
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Required Reading
| AA | Required Reading |
|---|---|
| S-INFO-061 | Note de cours - Intelligence Artificielle - Hadrien Mélot |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-INFO-061 | Not applicable |
Recommended Reading
| AA | |
|---|---|
| S-INFO-061 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| S-INFO-061 | Not applicable |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| S-INFO-061 | - Russel, S. and Norvig, P., Artificial Intelligence: A Modern Approach, 3ième édition, Pearson, 2010 - Talbi, E.-G., Metaheuristics: from design to implementation, Wiley, 2009 |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| S-INFO-061 | Authorized |