This course integrates meta-heuristics and reinforcement learning to provide comprehensive training in intelligent decision-making systems. The first part focuses on meta-heuristic algorithms for combinatorial optimization, including genetic algorithms, simulated annealing, tabu search, and swarm intelligence. The second part introduces reinforcement learning fundamentals, covering Markov decision processes, Q-learning, policy gradient methods, and deep reinforcement learning. Emphasis is placed on both theoretical understanding and practical implementation. Students will develop algorithms to solve real-world problems in their research domains, learning to select appropriate methods, tune parameters, and evaluate performance effectively.

This course integrates meta-heuristics and reinforcement learning to provide comprehensive training in intelligent decision-making systems. The first part focuses on meta-heuristic algorithms for combinatorial optimization, including genetic algorithms, simulated annealing, tabu search, and swarm intelligence. The second part introduces reinforcement learning fundamentals, covering Markov decision processes, Q-learning, policy gradient methods, and deep reinforcement learning. Emphasis is placed on both theoretical understanding and practical implementation. Students will develop algorithms to solve real-world problems in their research domains, learning to select appropriate methods, tune parameters, and evaluate performance effectively.

Walpole, R.E., Myers, R.H., Myers, S. L. and Ye, Keying (2016). Probability and
Statistics For Engineers and Scientists. (Global 9th edition). Pearson Education.

Ross, S. M. (2018). Introduction to Probability and Statistics (5th edition).
Elsevier Academic Press.

Walpole, R.E., Myers, R.H., Myers, S. L. and Ye, Keying (2016). Probability and
Statistics For Engineers and Scientists. (Global 9th edition). Pearson Education.

Ross, S. M. (2018). Introduction to Probability and Statistics (5th edition).
Elsevier Academic Press.