 | Study programme 2022-2023 | Français | |
 | Artificial Intelligence | ||
Programme component of Master's in Computer Science : Specialist Focus (CHARLEROI) (shift schedule) à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-INFOSP-102-C | 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-061 | Artificial Intelligence | 30 | 15 | 0 | 0 | 0 | Q2 | 100.00% |
| Programme component | ||
|---|---|---|
 | US-M1-INFOSP-104-C Data Structures | |
| | US-M1-INFOSP-900-C Algorithmics | |
Objectives of Programme's Learning Outcomes
- ills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
- ills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
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.
UE Content: description and pedagogical relevance
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 Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| S-INFO-061 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-INFO-061 |
|
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 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 |
Term 2 Assessment - type
| AA | Type(s) and mode(s) of Q2 assessment |
|---|---|
| S-INFO-061 |
|
Term 2 Assessment - comments
| AA | Term 2 Assessment - comments |
|---|---|
| S-INFO-061 | Written exam 85% Exercices 15% |
Term 3 Assessment - type
| AA | Type(s) and mode(s) of Q3 assessment |
|---|---|
| S-INFO-061 |
|
Term 3 Assessment - comments
| AA | Term 3 Assessment - comments |
|---|---|
| S-INFO-061 | Oral exam 100% |