News
Our Degree Programs
Establishment
Language of instruction
English
Teaching content
FINANCE
Training officer(s)
M.JOETS
Stakeholder(s)
M.JOETS
Présentation
Prerequisite
This course is a soft overview of the vast body of materials on machine learning and artificial intelligence that have proven to have a significant pratical value. It does not assume any high level of mathematical training, or even programming experience, but requires basic statistical knowledge. The content of the course being pratically oriented, basic concepts of Finance and Economics are required.
Goal
There are a number of learning objectives of this course.
Major learning objectives include:
1. Be able to define and explain in simple words key concepts of machine learning to financial and economic professionals (such as overfitting, underfitting, train and test sets, …)
2. Be able to explain the difference between machine learning and artificial intelligence frameworks.
3. Be able to discuss the range of methods/models that can be applied to specific business problems, along with their assumptions, strengths, and weaknesses.
4. Be able to define supervised, unsupervised, and reinforcement learnings approaches.
5. Be able to understand basic practice in data science (such as feature engineering, hyperparameter tuning, model performance,…).
Major learning objectives include:
1. Be able to define and explain in simple words key concepts of machine learning to financial and economic professionals (such as overfitting, underfitting, train and test sets, …)
2. Be able to explain the difference between machine learning and artificial intelligence frameworks.
3. Be able to discuss the range of methods/models that can be applied to specific business problems, along with their assumptions, strengths, and weaknesses.
4. Be able to define supervised, unsupervised, and reinforcement learnings approaches.
5. Be able to understand basic practice in data science (such as feature engineering, hyperparameter tuning, model performance,…).
Presentation
This course is designed to provide an overview of machine learning and artificial intelligence approaches and to demonstrate how those techniques are applied in decision making.
Course contents:
1. Overview of Machine Learning and Artificial Intelligence in day-to-day life.
1. Fundamental Supervised Learning algorithms with case studies
2. Fundamental Unsupervised Learning algorithms with case studies
3. Some words on Artificial Neural Network and Deep Learning
4. Best practice in Data Science
The course will conclude with some open discussions regarding the future of ML/AI in industry and society in general.
Course contents:
1. Overview of Machine Learning and Artificial Intelligence in day-to-day life.
1. Fundamental Supervised Learning algorithms with case studies
2. Fundamental Unsupervised Learning algorithms with case studies
3. Some words on Artificial Neural Network and Deep Learning
4. Best practice in Data Science
The course will conclude with some open discussions regarding the future of ML/AI in industry and society in general.
Modalités
Organization
| Type | Amount of time | Comment | |
|---|---|---|---|
| Présentiel | |||
| Cours interactif | 16,00 | ||
| Distanciel | |||
| Video-Conferences | 8,00 | ||
| Autoformation | |||
| Lecture du manuel de référence | 8,00 | ||
| E-Learning | 8,00 | ||
| Travail personnel | |||
| Charge de travail personnel indicative | 10,00 | ||
| Overall student workload | 50,00 | ||
Evaluation
Assessment will be made through three components:
- daily quizzes based on courses (15%);
- group project by 2 (15%, no programming);
- final exam (70%).
- daily quizzes based on courses (15%);
- group project by 2 (15%, no programming);
- final exam (70%).
| Control type | Duration | Amount | Weighting |
|---|---|---|---|
| Contrôle continu | |||
| QCM | 0,15 | 5 | 15,00 |
| Examen (final) | |||
| Examen écrit | 2,00 | 1 | 70,00 |
| Autres | |||
| Projet Collectif | 1,50 | 1 | 15,00 |
| TOTAL | 100,00 | ||
Ressources
Bibliography
The Hundred-Page Machine Learning Book (A. Burkov) - The Hundred-Page Machine Learning Book (A. Burkov)
The Hundred-Page Machine Learning Book (A. Burkov)
An Introduction to Statistical Learning with Applicaions in R (G. James, D. Witten, T. Hastie, and R. Tibshirani - An Introduction to Statistical Learning with Applicaions in R (G. James, D. Witten, T. Hastie, and R. TibshiraniAn Introduction to Statistical Learning with Applicaions in R (G. James, D. Witten, T. Hastie, and R. Tibshirani
Internet resources
Machine Learning Stanford (Coursera)
https://www.coursera.org/learn/machine-learning/
Towards Data Science (blog)
https://towardsdatascience.com/
Machine Learning Mastery (blog)
https://machinelearningmastery.com/