Applied Machine Learning for Games: A Graduate School Course
- URL: http://arxiv.org/abs/2012.01148v2
- Date: Fri, 1 Jan 2021 18:06:59 GMT
- Title: Applied Machine Learning for Games: A Graduate School Course
- Authors: Yilei Zeng, Aayush Shah, Jameson Thai, Michael Zyda
- Abstract summary: This paper describes our machine learning course designed for graduate students interested in applying recent advances of deep learning and reinforcement learning towards gaming.
Students enrolled in this course apply different fields of machine learning techniques such as computer vision, natural language processing, computer graphics, human computer interaction, robotics and data analysis to solve open challenges in gaming.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The game industry is moving into an era where old-style game engines are
being replaced by re-engineered systems with embedded machine learning
technologies for the operation, analysis and understanding of game play. In
this paper, we describe our machine learning course designed for graduate
students interested in applying recent advances of deep learning and
reinforcement learning towards gaming. This course serves as a bridge to foster
interdisciplinary collaboration among graduate schools and does not require
prior experience designing or building games. Graduate students enrolled in
this course apply different fields of machine learning techniques such as
computer vision, natural language processing, computer graphics, human computer
interaction, robotics and data analysis to solve open challenges in gaming.
Student projects cover use-cases such as training AI-bots in gaming benchmark
environments and competitions, understanding human decision patterns in gaming,
and creating intelligent non-playable characters or environments to foster
engaging gameplay. Projects demos can help students open doors for an industry
career, aim for publications, or lay the foundations of a future product. Our
students gained hands-on experience in applying state of the art machine
learning techniques to solve real-life problems in gaming.
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