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Who should attend
It is applicable for professionals engaged in the following areas:
- Data scientist
- Data analytics
- Software engineer
About the course
To realise the full potential of artificial intelligence, an agent (be it a piece of software or a robot) need to be able to adapt and continuously learn. These learning could be in to form of trial and error, observing an expert or by contemplation (self-play).
Reinforcement learning (RL) is the area of machine learning where agents learn to take actions in an environment so as to maximize some notion of reward. An example of this might be an autonomous robot vacuum (agent), learning and mapping an optimal path in a condo (taking actions in an environment) using the least amount of time and battery (maximizing the reward).
RL is typically associated with playing games such as chess, backgammon and more recently Go (see AlphaGO) and DOTA (see OpenAI Five). However RL has real-world applications where the environment is stochastic (uncertain); these include planning, resource scheduling, prescribing treatments, managing inventories and others.
At the end of the course, the participants will be able to:
- Explain the 2 basic concepts which are so fundamental to RL: Markov Decision/Reward Process and Bellman Optimality Equation
- Explain the different RL algorithms: Monte Carlo, Temporal-Difference learning, SARSA and Q-Learning
- Understand how to formalize a task as a reinforcement learning problem
- Apply different RL algorithms to enable an agent to learn how to make optimal actions/decision autonomously
Chuk is with the Advanced Technology Applications Practice for National University of Singapore, Institute of Systems Science (NUS-ISS). His current responsibilities includes developing courseware, and teaching graduate and public courses in enterprise software engineering, software architecture,...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.