This is a talk presented at Nanmath conference held Nov 4-7 2024 at ICTP, Cluj..
Talk materials: the slides of the presentation.
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This is a talk presented at Nanmath conference held Nov 4-7 2024 at ICTP, Cluj..
Talk materials: the slides of the presentation.
General chair with C. Lefter and A. Zalinescu of the conference FAAI24 « Foundations and applications of artificial intelligence » Iasi Oct 28-30 2024. At the conference I also serve as tutorial presenter.
Invited joint talk « Using LLMs techniques for time series prediction » with Pierre Brugiere presented at the 6th JP Morgan Global Machine Learning conference held in Paris, Oct 18th 2024
Talk materials: slides(click here) and here a link to the associated paper.
Responsable du cours : Gabriel Turinici
Contenu:
Bibliographie
pour théorie de gestion de portefeuille « actions » classique (proba historique) | livre du cours de M1 Mouvement Brownien et évaluation d’actifs dérivés |
Autres ressources pour le cours :
Co-organizer with E. Catinas of the « Numerical Analysis » sessions at « Le 16ème Colloque Franco-Roumain, Bucarest, Roumanie »
A short interview with Celine Loozen from ‘France Culture’ radio station within a radio program concerning AI and GAFAM ethics.
Link for the full radio broadcast
Interview with Celine Loozen : here (local version if necessary here)
Instructor: Gabriel TURINICI
1/ Introduction to reinforcement learning
2/ Theoretical formalism: Markov decision processes (MDP), value function ( Belman and Hamilton- Jacobi – Bellman equations) etc.
3/ Common strategies, building from the example of « multi-armed bandit »
4/ Strategies in deep learning: Q-learning and DQN
5/ Strategies in deep learning: SARSA and variants
6/ Strategies in deep learning: Actor-Critic and variants
7/ During the course: various Python and gym/gymnasium implementations
8/ Perspectives.
Multi Armed Bandit codes (MAB) : play MAB, solve MAB , solve MAB v2., policy grad from chatGPT to correct., policy grad corrected.
Bellman iterations: code to correct here, solution code here
Gym: play Frozen Lake (v2023) (version 2022)
Q-Learning : with Frozen Lake, python version or notebook version
-play with gym/Atari-Breakout: python version or notebook version
Deep Q Learning (DQN) : Learn with gym/Atari-Breakout: notebook 2024 and its version with smaller NN and play with result
Policy gradients on Pong adapted from Karpathy python or notebook
You can also load from HERE a converged version (rename as necessary) pg_pong_converged_turinici24
Notebook to use it: here (please send me yours if mean reward above 15!).
version 2023 : python or notebook Old version (2022): python or notebook
Projets : cf. Teams
Responsable: Gabriel TURINICI
Contenu
Note historique: nom du cours 2019/21: « Approches déterministes et stochastiques pour la valuation d’options »
Responsable de cours: Gabriel TURINICI
Contenu:
1 Introduction
2 EDO
3 Calcul de dérivée et contrôle
4 EDS
Bibliographie: poly distribué
Supports de cours: livre « Simulations numériques des problèmes dépendant du temps: appliquées à l’épidémiologie, l’intelligence artificielle et les finances«
Seances de TD: 2022/23
Implementations TP:
Intervention at the round table « AI and medias: when everything is accelerating » at the Dauphine Digital Days held Nov 20-22 2023 at the Université Paris Dauphine – PSL, Paris, France.
Video version: from minute 09:00
Conference badge 🙂