But du cours
This course aims to train students at the intersection of artificial intelligence (AI) and operations research (OR), with a particular focus on the diversity of problems that can be modeled using classical AI tools. A data scientist must be aware of the wide range of problems that can be addressed using formal logic methods, probabilistic methods, and exploratory methods. This course enables students to develop a deep understanding of fundamental principles and their application in various contexts.
Acquis d'apprentissage visés
- Conduct exploratory data analysis
- Process massive datasets using data mining techniques
- Visualize data
Prérequis
- Probability and random variables
- Stochastic processes
- DATA course (UE) from S5
Programme
AI and formal logic:
- Predicate logic and examples of applications
- Automated reasoning: forward chaining, backward chaining
- Reasoning integrated into large-scale systems (e.g., Watson)
Probabilistic approaches
- Causal models
- Bayesian networks
- Markov Decision Processes (MDP)
Search and optimization strategies
- Representation of possible solutions to a problem in the state space
- Depth-first and breadth-first (i.e., uninformed) search of a state space
- Heuristic (i.e., informed) search of a state space (e.g., A* search)
Modalités d'évaluation
Continuous assessments and practical evaluations.
Bibliographie
Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach, 4th US ed.
Supports
All documents used during teaching sessions. If needed, additional references may be introduced.