Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
6 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Deville Yves;
Language
English
Prerequisites
LSINF1121 : Programminng abilities in a high-level language, algorithmics and data structures
Main themes
- Problem solving by searching : formulating problems, uninformed and informed search search strategies, local search, evaluation of behavior and estimated cost, applications
- Constraint satisfaction : formulating problems as CSP, backtracking and constraint propagation, applications
- Games and adversarial search : minimax algorithm and Alpha-Beta pruning, applications
- Propositional logic : representing knowledge in PL, inference and reasoning, applications
- First-order logic : representing knowledge in FOL, inference and reasoning, forward and backward chaining, rule-based systems, applications
- Planning : languages of planning problems, search methods, planning graphs, hierarchical planning, extensions, applications
- AI, philosophy and ethics : "can machines act intelligently ?", "can machines really think ?", ethics and risks of AI, future of AI
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
|
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
- Introduction
- Search
- Informed search
- Local search
- Adversarial search
- Constraint Satisfaction Problem
- Logical Agent
- First-order logic and Inference
- Classical Planning
- Planning in the real world
- Learning from examples
- Philosophical foundations & Present and future of AI
Teaching methods
- Problem-Based Learning
- Learning by doing
- 5 assignments (one per two weeks)
- Team of two students
- Limited teaching (1 hour / week)
- Feed-back of problems (1/2 hour )
- Discussion of current problem (1/2 hour)
Evaluation methods
- Exam : 70%
-
Assignments : 30%.
Assignments must be personnal (team of 2). No collaboration between groups. No copying from Internet. Cheating = 0/20 all assignments. In case of failure of the missions the weight of this part will be more important. - Assignments may be realized only during the quadrimester of the course. It's not possible to realize the assignments during another quadrimester or for the exam session of september.
Online resources
Bibliography
- Stuart Russell, Peter Norvig, Artificial Intelligence : a Modern Approach, 3nd Edition, 2010, 1132 pages, Prentice Hall
- transparents en ligne
Faculty or entity
INFO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering
Master [120] in Biomedical Engineering
Master [120] in Computer Science and Engineering
Master [60] in Computer Science
Master [120] in Computer Science
Master [120] in Data Science: Information Technology