 | Study programme 2021-2022 | Français | |
 | Artificial Intelligence | ||
Programme component of Bachelor's in Computer Science à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-B3-SCINFO-005-M | Compulsory UE | MELOT Hadrien | S825 - Algorithmique |
|
| 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-014 | Artificial Intelligence | 30 | 15 | 0 | 0 | 0 | Q2 | 100.00% |
| Programme component | ||
|---|---|---|
 | US-B2-SCINFO-011-M Data Structures I | |
Objectives of Programme's Learning Outcomes
- Understand the fundamentals of computer science
- Solve exercises and computer problems by applying basic knowledge in the various disciplines of computer science
- Use and combine knowledge from different disciplines to solve multidisciplinary problems
- Understand the fundamentals related to scientific methods
- Develop skills of abstraction and modelling through a conceptual and scientific approach
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-014 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-INFO-014 |
|
Required Reading
| AA | Required Reading |
|---|---|
| S-INFO-014 | Note de cours - Intelligence Artificielle - Hadrien Mélot |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-INFO-014 | Not applicable |
Recommended Reading
| AA | |
|---|---|
| S-INFO-014 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| S-INFO-014 | Not applicable |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| S-INFO-014 | - 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-014 | Authorized |