Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what special Covid-19 assessment methods are possibly planned for the end of Q3 |
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Code | Type | Head of UE | Department’s contact details | Teacher(s) |
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UI-M2-IRIGIG-302-M | Optional UE | SIEBERT Xavier | F151 - Mathématique et Recherche opérationnelle | - SIEBERT Xavier
- MAHMOUDI Sidi
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
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| Anglais | 30 | 30 | 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.
- Evaluate the approach and results for their adaptation (modularity, optimisation, quality, robustness, reliability, upgradeability, etc.).
- 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.
- 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.
- Assess the approach and achievements, regulate them in view of the observations and feedback received.
- 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.
- Argue to and persuade customers, teachers and boards, both orally and in writing.
- 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.
Learning Outcomes of UE
Get familiar with the contemporary methods in machine learning (active learning, reinforcement learning, depp networks) Study these methods within the frameworks of statistical learning theory
Content of UE
active learning, reinforcement learning, deep networks, statistical learning theory
Prior Experience
basic knowledge in data mining / machine learning
python programming
mathematical bases
Type of Assessment for UE in Q1
- Presentation and/or works
- Written examination
Q1 UE Assessment Comments
personal work + written exam
Type of Assessment for UE in Q3
- Presentation and/or works
- Written examination
Q3 UE Assessment Comments
idem Q1
Type of Resit Assessment for UE in Q1 (BAB1)
Q1 UE Resit Assessment Comments (BAB1)
n/a
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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I-MARO-202 | - Cours magistraux
- Travaux pratiques
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I-ILIA-202 | - Cours magistraux
- Travaux pratiques
- Projet sur ordinateur
- Etudes de cas
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Mode of delivery
AA | Mode of delivery |
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I-MARO-202 | |
I-ILIA-202 | |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
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I-MARO-202 | Not applicable |
I-ILIA-202 | Not applicable |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
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I-MARO-202 | Not applicable |
I-ILIA-202 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
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I-MARO-202 | Not applicable |
I-ILIA-202 | Not applicable |
Grade Deferrals of AAs from one year to the next
AA | Grade Deferrals of AAs from one year to the next |
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I-MARO-202 | Authorized |
I-ILIA-202 | Authorized |
(*) 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