INTRODUCTION TO MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
Année du cours : 3 année(s)
Etablissement : IÉSEG School of Management
Langue : English
Formation(s) dans laquelle/lesquelles le cours apparait :
Période : S2
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.
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,…).
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.
