Study programme 2023-2024Français
Advanced Optimization for Data Science
Programme component of Master's in Computer Engineering and Management : Specialist Focus on Artificial Intelligence and Decision Aid (MONS) (day schedule) à la Faculty of Engineering

CodeTypeHead of UE Department’s
contact details 		Teacher(s)
UI-M1-IRIGIA-103-MCompulsory UEVANDAELE ArnaudF151 - Mathématique et Recherche opérationnelle
  • VANDAELE Arnaud
  • GILLIS Nicolas

Language
of instruction		Language
of assessment		 HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Anglais
Anglais263400055.002nd term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-MARO-232Topics in Convex Optimization816000Q240.00%
I-MARO-303First-Order Methods for Large Scale Machine Learning612000Q230.00%
I-MARO-018Optimization & Operational Research126000Q230.00%

Programme component

Objectives of Programme's Learning Outcomes

  • Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out computer and management engineering missions, with a focus on Innovation and Information Systems, using their expertise and adaptability.
    • Master and appropriately mobilise knowledge, models, methods and techniques related to the improvement of decision and management processes, mastery of mathematical modelling and optimisation algorithms, analysis of large volumes of data, mastery of Web and multimedia tools, design and operation of distributed and mobile computing systems, management of a software project, innovative management of a company and/or project team, information systems (data mining, database, cloud computing, etc.) and management of technological innovation.
    • 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.
    • Assess the validity of models and results in view of the state of science and characteristics of the problem.
  • 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).
    • Identify complex problems to be solved and develop the specifications with the client by integrating 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.

Learning Outcomes of UE

see the different AA

UE Content: description and pedagogical relevance

Global mark.
A minimum score to be achieved in each of the AAs will be communicated.

Prior Experience

Numerical Analysis, optimization and programming skills

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
I-MARO-232
  • Cours magistraux
  • Conférences
  • Travaux de laboratoire
  • Projet sur ordinateur
I-MARO-303
  • Cours magistraux
  • Travaux pratiques
  • Projet sur ordinateur
I-MARO-018
  • Cours magistraux
  • Exercices dirigés
  • Utilisation de logiciels
  • Démonstrations

Mode of delivery

AAMode of delivery
I-MARO-232
  • Face-to-face
I-MARO-303
  • Face-to-face
I-MARO-018
  • Face-to-face

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-MARO-232Not applicable
I-MARO-303Slides and other references available on Moodle
I-MARO-018Not applicable

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-MARO-232Not applicable
I-MARO-303Bottou, L., Curtis, F. E., & Nocedal, J. (2018). Optimization methods for large-scale machine learning. Siam Review, 60(2), 223-311.  Newton, D., Yousefian, F., & Pasupathy, R. (2018). Stochastic Gradient Descent: Recent Trends. In Recent Advances in Optimization and Modeling of Contemporary Problems (pp. 193-220). INFORMS.
I-MARO-018Not applicable

Other Recommended Reading

AAOther Recommended Reading
I-MARO-232Not applicable
I-MARO-303Not applicable
I-MARO-018Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
I-MARO-232Unauthorized
I-MARO-303Unauthorized
I-MARO-018Unauthorized

Term 2 Assessment - type

AAType(s) and mode(s) of Q2 assessment
I-MARO-232
  • Written examination - Face-to-face
  • Production (written work, report, essay, collection, product, etc.) - To be submitted in class
  • Oral examination - Face-to-face
  • Seminar participation - Face-to-face
  • Graded assignment(s) - Face-to-face
  • Practical exam - Face-to-face
I-MARO-303
  • Written examination - Remote
I-MARO-018
  • Written examination - Face-to-face
  • Production (written work, report, essay, collection, product, etc.) - To be submitted in class
  • Oral examination - Face-to-face
  • Oral presentation - Face-to-face
  • Seminar participation - Face-to-face
  • Graded assignment(s) - Face-to-face
  • Practical exam - Face-to-face

Term 2 Assessment - comments

AATerm 2 Assessment - comments
I-MARO-232not applicable
I-MARO-303This AA is evaluated via the project
I-MARO-018The evaluation methods are likely to be adjusted according to the context imposed by the health measures.
These evaluation methods can be composed of personal work (and / or in groups), presentations, written exam, oral exam.
For students who have not respected the 80% participation rule, the evaluation will be based on a specific exam to be described by the professor before the exam
In all cases (physically or remotely), if several parts are to be completed during the evaluation, an exclusion mark (the final mark is equal to the minimum mark of the different parts) will possibly be introduced (if so, it will be announced before the exam).

Term 3 Assessment - type

AAType(s) and mode(s) of Q3 assessment
I-MARO-232
  • Written examination - Face-to-face
  • Production (written work, report, essay, collection, product, etc.) - To be submitted in class
  • Oral examination - Face-to-face
  • Seminar participation - Face-to-face
  • Graded assignment(s) - Face-to-face
  • Practical exam - Face-to-face
I-MARO-303
  • Written examination - Remote
I-MARO-018
  • Written examination - Face-to-face
  • Production (written work, report, essay, collection, product, etc.) - To be submitted in class
  • Oral examination - Face-to-face
  • Oral presentation - Face-to-face
  • Seminar participation - Face-to-face
  • Graded assignment(s) - Face-to-face
  • Practical exam - Face-to-face

Term 3 Assessment - comments

AATerm 3 Assessment - comments
I-MARO-232The evaluation methods are likely to be adjusted according to the context imposed by the health measures.
These evaluation methods can be composed of personal work (and / or in groups), presentations, written exam, oral exam.
For students who have not respected the 80% participation rule, the evaluation will be based on the theoretical knowledge of the course.
I-MARO-303idem Q1
I-MARO-018The evaluation methods are likely to be adjusted according to the context imposed by the health measures.
These evaluation methods can be composed of personal work (and / or in groups), presentations, written exam, oral exam.
For students who have not respected the 80% participation rule, the evaluation will be based on a specific exam to be described by the professor before the exam
In all cases (physically or remotely), if several parts are to be completed during the evaluation, an exclusion mark (the final mark is equal to the minimum mark of the different parts) will possibly be introduced (if so, it will be announced before the exam).
(*) 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
More information on Higher Education Structure (’Paysage’ Decree)
Date de dernière mise à jour de la fiche ECTS par l'enseignant : 16/05/2023
Date de dernière génération automatique de la page : 27/04/2024
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Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be