This course introduces deep reinforcement learning (RL), one of the most modern techniques of machine learning. Deep RL has attracted the attention of many researchers and developers in recent years due to its wide range of applications in a variety of fields such as robotics, robotic surgery, pattern recognition, diagnosis based on medical image, treatment strategies in clinical decision making, personalized medical treatment, drug discovery, speech recognition, computer vision, and natural language processing. Deep RL is often seen as the third area of machine learning, in addition to supervised and unsupervised algorithms, in which learning of an agent occurs as a result of its own actions and interaction with the environment. Generally, such learning processes do not need to be guided externally, but it has been difficult until recently to use RL ideas practically. This course primarily focuses on problems that emerge in healthcare and life science applications.
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