Contexte
The MScT AI MaQI is a unique programme combining know-how in financial markets and in artificial intelligence. Students following this program will learn how to combine machine learning and quantitative finance to find new solutions to the problems of the industry in interacting with markets and in building systematic investment strategies. They will become experts capable of implementing innovative learning-based solutions for applications in financial markets. This programme offers practical training with projects, internships and hackathons to prepare students for the real needs of industry with an emphasis on financial know-how, understanding of learning algorithms, and getting familiar with new kinds of data like satellite images or maritime traffic for investment and risk management.
domaines d'enseignement
Mathématiques appliquées, Informatique.métiers
After graduating from the MSc in "AI MaQI", students will be well-equipped to pursue a variety of career paths:
- In the "AI Lab" or "Data Science Lab" or a bank, an asset manager, a hedge fund or any other financial institution
- Be the "AI expert" of a team of quantitative analysts, "quants", or "strats"
- Lead the projects for financial markets of a startup
It is perfectly possible to pursue a PhD after this program, especially in partnership with a financial institution.
Parcours
- MScT-MaQI-GD1A AI for Markets and Quantitative Investment (AI-MaQI) - 1ère année Master of Science and Technology (Graduate degree)
- MScT-MaQI-S1 MScT MaQI- Semestre 1
- APM_51460_EP Python for Data Science
- MScT-MaQI-S2 MScT MaQI - Semestre 2
- INT_54496_EP Internship MaQI M1
- MScT-MaQI-S1 MScT MaQI- Semestre 1
- MScT-MaQI-GD2A AI for Markets and Quantitative Investment (AI-MaQI) - 2ème année Master of Science and Technology (Graduate degree)
- MScT-MaQI-S3 MScT MaQI - Semestre 3
- APM_53442_EP Learning to Replicate Risks
- APM_53443_EP Risk Taking in Financial Markets
- APM_53444_EP Compression and High Dimension in Finance
- APM_53445_EP Machine Learning for Financial Time Series
- APM_53446_EP Piscine/Hackathon/AI workflow
- APM_53447_EP Seminar: uses cases and papers Part 1
- MScT-MaQI-S4 MScT MaQI - Semestre 4
- APM_54440_EP GenAI for clients and products
- APM_54441_EP IA for better risk control
- APM_54442_EP IA for alternative data
- APM_54443_EP Seminar: uses cases and papers Part 2
- INT_54497_EP Internship MaQI M2
- MScT-MaQI-S3 MScT MaQI - Semestre 3
Unités d'enseignement
UE | Type d'enseignement | Domaines | Catégorie d'UE | Credit Ects | Volume horaire | Responsables | Periode de programmation | Site pédagogique |
---|---|---|---|---|---|---|---|---|
APM_51460_EP Python for Data Science | Cours scientifiques | Mathématiques appliquées | Mathurin Massias | X-AN3-P1 | ||||
APM_53442_EP Learning to Replicate Risks | Cours scientifiques | Mathématiques appliquées | 3 | |||||
APM_53443_EP Risk Taking in Financial Markets | Cours scientifiques | Mathématiques appliquées | 3 | |||||
APM_53444_EP Compression and High Dimension in Finance | Cours scientifiques | Mathématiques appliquées | 3 | |||||
APM_53445_EP Machine Learning for Financial Time Series | Cours scientifiques | Mathématiques appliquées | 3 | |||||
APM_53446_EP Piscine/Hackathon/AI workflow | Cours scientifiques | Mathématiques appliquées | 5 | |||||
APM_53447_EP Seminar: uses cases and papers Part 1 | Cours scientifiques | 3 | ||||||
APM_54440_EP GenAI for clients and products | Cours scientifiques | Mathématiques appliquées | 3 | |||||
APM_54441_EP IA for better risk control | Cours scientifiques | Mathématiques appliquées | 3 | |||||
APM_54442_EP IA for alternative data | Cours scientifiques | Mathématiques appliquées | 3 | |||||
APM_54443_EP Seminar: uses cases and papers Part 2 | Cours scientifiques | 2 | ||||||
INT_54496_EP Internship MaQI M1 | Stage | |||||||
INT_54497_EP Internship MaQI M2 | Stage | 25 |
