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Pattern Recognition, Medical Imaging, Machine Intelligence, Computer Vision Hamid R. Tizhoosh
Faculty of Engineering
University of Waterloo


Pattern Recognition, Computer Vision,
Medical Imaging, Machine Intelligence
Hamid R. Tizhoosh
Teaching
Computer Vision
Machine Intelligence
Data Structures
SD750 - OBL
Research
Pattern Recoginition
Computer Vision
Machine Intelligence
Terahertz Imaging
Health Engineering
Opposition-Based Learning
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Former Students
Projects
LORNET
Prostate Cancer
Breast Cancer
Radiation Therapy
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University of Waterloo :: Faculty of Engineering :: Systems Design

Research …
Reinforcement Learning

Control tasks and optimal outputs can be reinforced by means of reward and punishment. Reinforcement agents interact with the environment or system they are supposed to control. The agent takes meaningful actions which can change the system state and receive feedback. Desired behavior results in rewarding reinforcement signal; the gent learns what to do step by step.

Reinforcement Learning Agents

Fields & Methodologies

  • Dynamic Programming vs. Monte Carlo

  • Q-Learning

  • Temporal Differencing

  • RL Agents with Fuzzy Reinforcement

  • Opposition-Based RL Agents

Applications

  • Learning Computer Vision Tasks

  • Bandwidth Allocation in Internet

  • RL Agents for Control of Industrial Systems

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Learning from Interaction

"The idea that we learn by interacting with our environment is probably the first to occur to us when we think about the nature of learning. When an infant plays, waves its arms, or looks about, it has no explicit teacher, but it does have a direct sensori-motor connection to its environment. Exercising this connection produces a wealth of information about cause and effect, about the consequences of actions, and about what to do in order to achieve goals. Throughout our lives, such interactions are undoubtedly a major source of knowledge about our environment and ourselves. Whether we are learning to drive a car or to hold a conversation, we are all acutely aware of how our environment responds to what we do, and we seek to influence what happens through our behavior. Learning from interaction is a foundational idea underlying nearly all theories of learning and intelligence."

From: Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto, MIT Press, Cambridge, MA, 1998


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