How can a computer learn to diagnose cancer?
How can a robotic assistant learn to adapt to the specific habits of their owners?
Machine Learning is the study of how computers can learn complex concepts from data and experience, and seeks to answer the fundamental research questions underpinning the challenges outlined above.
The field of machine learning crosses a wide variety of disciplines that use data to find patterns in the ways both living systems, such as the human body and artificial systems, such as robots, are constructed and perform.
Whether it’s being applied to analyze and learn from medical data, or to model financial markets, or to create autonomous vehicles, machine learning builds and learns from both algorithm and theory to understand the world around us and create the tools we need and want.
Specific Machine Learning research topics in Computer Science include learning conceptual structures through developmental processes; improving control of stochastic and nonlinear dynamic systems through reinforcement feedback; learning robot control strategies; finding patterns in large bodies of data represented in graphical form, including social networks; extracting or retrieving information in natural language; classification of genetic data; and using learning methods for improving discrete optimization algorithms.
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