EM054M6G

Semestre
B
Discipline
Information systems management
Volume horaire
27 H
Nombre de places
45
Ouvert aux visitants
Oui
Langue
Responsable
Samia CHEHBI GAMOURA


Contribution pédagogique du cours au programme

Aucune contribution pédagogique associé à ce cours pour ce programme.

Descriptif

Today, artificial intelligence and data systems are pervading all systems in organizations. Management and business sectors are integrating artificial intelligence techniques and algorithms into all activities at all levels. Many decisions in managers' daily tasks are now supported and automated or semi-automated by AI. Artificial intelligence" is a paradigm that covers many techniques and approaches. Some of them are used more in business and management, such as marketing, finance, merchandising, manufacturing, logistics, human resources, etc.

Organisation pédagogique

Face-to-face

- Lectures
- Tutorials

In group

- Exercises
- Oral presentations
- Case studies/texts

Interaction

- Discussions/debates

Others

Aucun élément de cette liste n'a été coché.

Objectifs pédagogiques

Cognitive domain

A l'issue du cours, l'étudiant(e) devrait être capable de / d'...
  • - (niv. 1) identify the main algorithms and techniques of Artificial Intelligence that are known in the business sector
  • - (niv. 2) give examples about the different AI techniques in business cases
  • - (niv. 3) manipulate some datasets in AI integration cases
  • - (niv. 4) analyze situations where automated decisions are needed and thus AI maybe applied
  • - (niv. 5) interpret situations and results when applying AI in some business cases

Affective domain

A l'issue du cours, l'étudiant(e) devrait être capable de / d'...
  • - (niv. 1) choose and apply the main concepts about Artificial Intelligence in the Business contexts
  • - (niv. 2) discuss the business models to integrate AI techniques
  • - (niv. 3) explain some relevant cases of use that may need AI integration

Objectifs de développement durable abordés

Aucun objectif de développement durable n'a été coché.

Plan / Sommaire

I. Business data 1. Turing machine 2. Automated decision-making 3. Data-driven systems II. Analytics 1. What and why 2. Analytics mutations 2.1. Traditional analytics 2.2.1. OLAP 2.2.2. Business Intelligence 2.2. Advanced analytics 2.2.1. Data Mining 2.2.2. Artificial Intelligence III. Artificial intelligence 1. What and why 2. History 3. Design patterns 4. AI branches 5. Machine learning 6. Deep learning 7. Applications 7.1. in marketing: association rule algorithm 7.2. in warehousing: decision tree algorithm IV. The future of data and artificial intelligence in business? 1. Existing challenges and opportunities 2. Future challenges and opportunities

Prérequis nécessaires

Connaissances en / Notions clés à maîtriser

Fundamentals in organizations and management – Skills about business concepts - Skills in MS Excel

Supports pédagogiques

Mandatory tools for the course

- Computer
- Calculator
- Reference manuals

Documents in all formats


- Case studies/texts
- Worksheets

Moodle platform

- Upload of class documents
- Interface to submit coursework
- Assessments
- Coaching/mentoring

Software

Aucun élément de cette liste n'a été coché.

Additional electronic platforms


- Other :

Bibliographie recommandée

Ouvrages principaux

Book: Rose, D. (2018). Artificial Intelligence for Business What You Need to Know about Machine Learning and Neural Networks. (available in Moodle) Book: Deshpande, A., & Kumar, M. (2018). Artificial intelligence for big data: Complete guide to automating big data solutions using artificial intelligence techniques. Packt Publishing Ltd. (available in Moodle) Book: A. J. Gutman, J. Goldmeier (2021). Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning (available in Moodle)

Littérature complémentaire

Book: Finlay, S. (2021). Artificial intelligence and machine learning for business: a no-nonsense guide to data driven technologies (No. 4th ed). Relativistic. Book: Panda, S. K., Mishra, V., Balamurali, R., & Elngar, A. A. (Eds.). (2021). Artificial Intelligence and Machine Learning in Business Management: Concepts, Challenges, and Case Studies. CRC Press.

Travaux de recherche de l'EM : Veillez à mobiliser au moins une ressource

Peuvent être renseignés les manuels coordonnés, les traductions de manuel, les études de cas traduites etc…
CHEHBI GAMOURA S. (2021). Predictive Reinforcement Learning Algorithm for Unstructured Business Process Optimization: Case of Human Ressources Process. International Journal of Spatio-Temporal Data Science, 1 (n° 2). CHEHBI GAMOURA S., DERROUICHE R., DAMAND D., BARTH M. (2020). Insights from Big Data Analytics in Supply Chain Management: An All-Inclusive Literature Review Using the SCOR Model. Production Planning and Control, 31 (n° 5) [CNRS cat.2, FNEGE cat.2, HCERES cat.A] Impact Factor. 4. Review. Artificial Intelligence and Applied Mathematics in Engineering Problems, Cham, Switzerland, Springer Nature, 1-16.

Modalités d'évaluation

Liste des modalités d'évaluation

Evaluation intermédiaire / contrôle continu 1Autre (date, contrôle surprise...) : automared tests at the end of each session case study at the 4th session
Ecrite et orale (30 min) / en groupe / Anglais / pondération : 30 %
Précisions : Automated tests individually a case study in group
Cette évaluation sert à mesurer LO1.1, LO1.2, LO1.3, LO2.1, LO2.3, LO4.1, LO4.2
Evaluation finaleDernière séance
Ecrite (120 min) / individuelle / Anglais / pondération : 70 %
Cette évaluation sert à mesurer LO1.1, LO1.2, LO1.3, LO2.3, LO4.1, LO4.2
Aucune modalité d'évaluation n'a pour le moment été attribuée à ce cours.