Code
MGFE MIS 4404
Level
M1
Field
Systèmes d’information
Language
Anglais/English
ECTS Credits
2
Class hours
18
Total student load
50
Program Manager(s)
Department
- Data analytics, Économie et Finances
Educational team
Introduction to the module
Over the next weeks, we will explore the foundations, methods, and applications of AI and data-driven decision-making. This course is designed to give you both the conceptual understanding and hands-on experience you need to grasp how AI is shaping businesses, industries, and society at large.
We will begin by asking some essential questions: What is AI? How do machines learn? What makes AI different from traditional programming? From there, we’ll move into practical techniques such as search and optimization, machine learning (supervised, unsupervised, and reinforcement), and deep learning with neural networks. Along the way, we will also look at real-world applications in business, marketing, finance, and operations, as well as the ethical challenges and risks associated with AI systems.
This course will not only focus on theory but also on practice. Through interactive exercises, games, and coding projects in Python and Google Colab, you’ll see how algorithms work under the hood and how they can be applied to real problems. You’ll also work in groups on a final project, where you will analyze a company case study: identifying business challenges, proposing AI solutions, and evaluating the required resources. Your work will culminate in a written report and a presentation.
NB: This class requires high motivation and implication levels from the students. You will need to program all along. You will be needing a personal computer, Tablets are possible but no advised.
Learning goals/Programme objectives
- LG1 Being able to extend digital intelligence through its different dimensions
- LG4 Having access to different cross disciplinary management approaches and tools
Learning objectives/Intended learning outcomes
- 1 - Being able to extend digital intelligence through its different dimensions
- 1.1 - Develop digital citizenship and prosperity
- 1.2 - Develop digital creativity for the individual and the organizational
Rubrics
- Statistics
- Data visualization
- Linear regressions
- Understand the main branches of AI and their applications
- Apply fundamental algorithms for problem-solving, optimization, and learning.
- Use tools like Kaggle datasets and TensorFlow to test and experiment with models.
- Critically assess the risks, biases, and ethical considerations of AI.
- Connect AI methods with real-world business strategies.
Content : structure and schedule
This course will take place in 9 sessions (might change)
1- Introduction to Big Data and Artificial Intelligence
2- Knowledge, Uncertainty, and Probability
3- Linear Regression and Machine Learning
4- Causality and Correlation
5- Supervised Models
6- Unsupervised Models
7- Deep Learning
8- AI, Ethics, Risks, and Regulation
9- Project Presentations
Sustainable Development Goals
ODD: 4, 8, 9
Number of SDG's addressed among the 17
3 ODD
Learning delivery
synchrone
Pedagogical methods
This course is based on a learning-by-doing approach. The classes will include a theoretical component, which will cover the essential concepts, as well as a practical component where the theory will be applied through programming exercises.
Evaluation and grading system and catch up exams
Written project = 75%
Presentation = 25%
Catch up exam: A new written projetc
Module Policies
Professor-Student Communication
● The professor will contact the students through their school email address (IMT-BS/TSP) and the Moodle portal. No communication via personal email addresses will take place. It is the student responsibility to regularly check their IMT-BS/TSP mailbox.
● Students can communicate with the professor by emailing him/her to his institutional address. If necessary, it is possible to meet the professor in his office during office-hours or by appointment.
Students with accommodation needs
If a student has a disability that will prevent from completing the described work or require any kind of accommodation, he may inform the program director (with supporting documents) as soon as possible. Also, students are encouraged to discuss it with the professor.
Class behavior
● Out of courtesy for the professor and classmates, all mobile phones, electronic games or other devices that generate sound should be turned off during class.
● Students should avoid disruptive and disrespectful behavior such as: arriving late, leaving early, careless behavior (e.g. sleeping, reading a non-course material, using vulgar language, over-speaking, eating, drinking, etc.). A warning may be given on the first infraction of these rules. Repeated violators will be penalized and may face expulsion from the class and/or other disciplinary proceedings.
● The tolerated delay is 5 minutes. Attendance will be declared on Moodle during these 5 minutes via a QR code provided by the teacher at each course start.
● Student should arrive on time for exams and other assessments. No one will be allowed to enter the classroom once the first person has finished the exam and left the room. There is absolutely no exception to this rule. No student can continue to take an exam once the time is up. No student may leave the room during an examination unless he / she has finished and handed over all the documents.
● In the case of remote learning, the student must keep his camera on unless instructed otherwise by the professor.
Honor code
IMT-BS is committed to a policy of honesty in the academic community. Conduct that compromises this policy may result in academic and / or disciplinary sanctions. Students must refrain from cheating, lying, plagiarizing and stealing. This includes completing your own original work and giving credit to any other person whose ideas and printed materials (including those from the Internet) are paraphrased or quoted directly. Any student who violates or helps another student violate academic behavior standards will be penalized according to IMT-BS rules.
Textbook Required and Suggested Readings
Business Data Science, Matt Taddy
Artificial Intelligence: A Modern Approach, Russell, S., & Norvig, P.
Keywords
Artificial Intelligence, Big Data, Data Science, Machine Learning
Prerequisites
The students should have already done at least a class in data science and have already worked in a programming language.