خيارات التسجيل
Learning Objectives
By the end of this course, students will be able to:
- Understand the theoretical foundations of sequential decision-making.
- Model problems using Markov Decision Processes (MDPs).
- Master classical and deep reinforcement learning algorithms.
- Implement and evaluate intelligent agents in simulated environments.
- Grasp the limitations, challenges, and research perspectives in reinforcement learning.
Prerequisites
- Probability and statistics (L3/M1 level)
- Linear algebra and optimization
- Python programming (with Numpy/Pandas)
- Basic knowledge of supervised and unsupervised learning
- (Optional but recommended) Basic understanding of deep learning (neural networks)
