Code | Type | Head of UE | Department’s contact details | Teacher(s) |
---|
UI-M2-IRIGIG-608-M | Optional UE | DUTOIT Thierry | F105 - Information, Signal et Intelligence artificielle | - BEN TAIEB Souhaib
- DUPONT Stéphane
- MAHMOUDI Sidi
- SIEBERT Xavier
- DUTOIT Thierry
|
Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|
| Français | 12 | 36 | 0 | 0 | 0 | 5 | 5.00 | 1st term |
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.
- Deliver a solution selected in the form of diagrams, graphs, prototypes, software and/or digital models.
- 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.
- Identify and discuss possible applications of new and emerging technologies in the field of information technology and sciences and quantifying and qualifying business management.
- Assess the validity of models and results in view of the state of science and characteristics of the problem.
- Plan, manage and lead projects in view of their objectives, resources and constraints, ensuring the quality of activities and deliverables.
- Define and align the project in view of its objectives, resources and constraints.
- Respect deadlines and timescales
- Communicate and exchange information in a structured way - orally, graphically and in writing, in French and in one or more other languages - scientifically, culturally, technically and interpersonally, by adapting to the intended purpose and the relevant public.
- Use and produce scientific and technical documents (reports, plans, specifications) adapted to the intended purpose and the relevant public.
- Adopt a professional and responsible approach, showing an open and critical mind in an independent professional development process.
- Exploit the different means available in order to inform and train independently.
- Contribute by researching the innovative solution of a problem in engineering sciences.
- Construct a theoretical or conceptual reference framework, formulate innovative solutions from the analysis of scientific literature, particularly in new or emerging disciplines.
- Develop and implement conceptual analysis, numerical modelling, software implementations, experimental studies and behavioural analysis.
- Collect and analyse data rigorously.
- Adequately interpret results taking into account the reference framework within which the research was developed.
- Communicate, in writing and orally, on the approach and its results in highlighting both the scientific criteria of the research conducted and the theoretical and technical innovation potential, as well as possible non-technical issues.
Learning Outcomes of UE
Practical (hands-on) knowledge of the AI tools (mostly deep nets and deep reinforcement learning); knowledge og the state-of-the-art deep net architectures for solving AI problems.
UE Content: description and pedagogical relevance
Three applicative challenges in AI, coming from various domains are proposed. For each challenge, 3 hours are devoted to theory, followed by two 3-hours co-working sessions in teams, and a report is prepared by students at home.
A series of seminars are organized in parallel, on transdisciplinary topics related to AI.
All activities are proposed in evenings (in the same format as the Mons AI Meetups launched in 2017).
They are also accessible to people registered to the Certificat d'Université en Intelligence Artificielle (See this page for more info, especially the Programme and Structure tab)
Prior Experience
Basics of computer science and programming languages (Python)
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
---|
I-ISIA-200 | - Cours magistraux
- Projet sur ordinateur
|
I-ISIA-201 | - Ateliers et projets encadrés au sein de l'établissement
|
Mode of delivery
AA | Mode of delivery |
---|
I-ISIA-200 | |
I-ISIA-201 | |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
---|
I-ISIA-200 | Not applicable |
I-ISIA-201 | Not applicable |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
---|
I-ISIA-200 | Not applicable |
I-ISIA-201 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
---|
I-ISIA-200 | Not applicable |
I-ISIA-201 | Not applicable |
Grade Deferrals of AAs from one year to the next
AA | Grade Deferrals of AAs from one year to the next |
---|
I-ISIA-200 | Authorized |
I-ISIA-201 | Authorized |
Term 1 Assessment - type
AA | Type(s) and mode(s) of Q1 assessment |
---|
I-ISIA-200 | - Production (written work, report, essay, collection, product, etc.) - To be submitted online
|
I-ISIA-201 | - Production (written work, report, essay, collection, product, etc.) - To be submitted online
|
Term 1 Assessment - comments
AA | Term 1 Assessment - comments |
---|
I-ISIA-200 | Challenge reports, 100%. Failure to report on one of the challenges results in a 0 for the whole UE |
I-ISIA-201 | Seminars will be noted on the basis of attendance summaries provided by students. Failure to submit seminar summaries will result in a 0 for the entire UE |
Resit Assessment - Term 1 (B1BA1) - type
AA | Type(s) and mode(s) of Q1 resit assessment (BAB1) |
---|
I-ISIA-200 | - Production (written work, report, essay, collection, product, etc.) - To be submitted online
|
I-ISIA-201 | - Production (written work, report, essay, collection, product, etc.) - To be submitted online
- N/A - Néant
|
Term 3 Assessment - type
AA | Type(s) and mode(s) of Q3 assessment |
---|
I-ISIA-200 | - Production (written work, report, essay, collection, product, etc.) - To be submitted online
|
I-ISIA-201 | - Production (written work, report, essay, collection, product, etc.) - To be submitted online
|
Term 3 Assessment - comments
AA | Term 3 Assessment - comments |
---|
I-ISIA-200 | same as Q1 |
I-ISIA-201 | Seminars will be noted on the basis of attendance summaries provided by students. |
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term