 | Study programme 2023-2024 | Français | |
 | Artificial Intelligence | ||
Programme component of Bachelor's in Computer Science (MONS) (day schedule) 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 | 20 | 0 | 0 | 0 | 5 | 5.00 | 1st term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| S-INFO-014 | Artificial Intelligence classical approaches | 22 | 12 | 0 | 0 | 0 | Q1 | 66.00% |
| S-INFO-114 | Introduction to Deep Learning | 8 | 8 | 0 | 0 | 0 | Q1 | 34.00% |
| 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 and Deep Learning. The students will be able to identify when a particular method can be applied.
UE Content: description and pedagogical relevance
See both learning activities.
Prior Experience
Knowledge of a programming language (e.g., Python ou Java) and of basic data structures (lists, trees, graphs).
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| S-INFO-014 |
|
| S-INFO-114 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-INFO-014 |
|
| S-INFO-114 |
|
Required Reading
| AA | Required Reading |
|---|---|
| S-INFO-014 | Note de cours - Approches Classiques de l'Intelligence Artificielle - Hadrien Mélot |
| S-INFO-114 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-INFO-014 | Not applicable |
| S-INFO-114 | All learning resources and tools required for this cours are available via Moodle, the online e-learning platform of UMONS. |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| S-INFO-014 | Not applicable |
| S-INFO-114 | Additional recommended material is also accessible through Moodle, the online e-learning platform of UMONS. |
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 |
| S-INFO-114 | Not applicable |
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 |
| S-INFO-114 | Unauthorized |
Term 1 Assessment - type
| AA | Type(s) and mode(s) of Q1 assessment |
|---|---|
| S-INFO-014 |
|
| S-INFO-114 |
|
Term 1 Assessment - comments
| AA | Term 1 Assessment - comments |
|---|---|
| S-INFO-014 | Written exam (85%) and online assignements (15%) |
| S-INFO-114 | Cfr. types and modes of assessment |
Resit Assessment - Term 1 (B1BA1) - type
| AA | Type(s) and mode(s) of Q1 resit assessment (BAB1) |
|---|---|
| S-INFO-014 |
|
| S-INFO-114 |
|
Term 3 Assessment - type
| AA | Type(s) and mode(s) of Q3 assessment |
|---|---|
| S-INFO-014 |
|
| S-INFO-114 |
|
Term 3 Assessment - comments
| AA | Term 3 Assessment - comments |
|---|---|
| S-INFO-014 | Oral exam 100% |
| S-INFO-114 | Cfr. types and modes of assessment |