Students who have a solid background in technical aspects of human-robot or human-computer interaction, social robotics, mobile manipulation, human and multi-robot systems, computational learning, artificial intelligence, game design, and/or interactive characters.
This contribution presents a Web-based learning system for system identification, which integrates computational identification tools.
The computational intelligence system we developed here is a machine learning system rather a medical expert system (which is a much more sophisticated system governed by the rules based on the opinions from the experts in a specified field).
To examine the complexity of the human brainstem dopamine system, we identified behavioral correlates and mesencephalic dopaminergic sources of three computational learning signals: signed TD errors, unsigned TD errors, and fictive errors.
Their Vapnik-Chervonenkis theory of computational learning is well known today.
His research focuses on computational learning theory and computational complexity.
The proposed learning system provides the Rubicon ecology with a set of generalpurpose learning services which can be used to learn generic computational tasks that involve predicting information of interest based on dynamic sensorial input streams.
The second type of computational learning signal that we studied was a counterfactual learning signal.
Imagining and building data-intensive brain science capabilities, neuroinformatics, computational neuroscience, and machine learning systems to analyze and model neuroscience datasets of all sizes.
However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive workloads of existing distributed deep learning systems.
In three recent papers he, Sendek and Cheon describe how they use machine learning – computational systems that teach themselves – to analyze the physical and chemical properties of thousands of materials.
