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Genetic Algorithms in Data Science: Some Theoretical Aspects of Employing GA Reinforcement Learning
This presentation will introduce the concepts of genetic network programming and its benefits in improving the computational complexity of learning algorithms. In particular, we will focus on graph representations of problems particularly suited for reinforcement learning.
6:00 - 6:45 - Eat, Meet, and Greet
6:45 - 7:00 - Introduction / Announcements
7:00 - 7:20 - GCU Student Demos/Presentations
7:20 - 8:30 - Dr. Artzi Presents on Genetic Algorithms
Isac Artzi, PhD, Associate Professor & Computer Science Program Lead at Grand Canyon University, College of Science, Engineering, and Technology (https://www.linkedin.com/in/isacartzi/)
Dr. Artzi teaches Computer Science courses in Artificial Intelligence, Big Data, Analytics, Mathematical Modeling, MBA, and MIS courses. He mentors senior capstone projects and conducts research on adaptive algorithms, machine learning, and virtual reality.
Dr. Artzi's career spans 30 years in the industry, research, military training, and international travel. Dr. Artzi's achievements include his Lynda.com C Programming class with over 1,200,000 student views in 142 countries, two software patents with Silicon Valley startup AppStream (acquired by Symantec), satellite-based distance learning systems in Israel (Gilat Satellite, Ltd), and published research in educational technology at Michigan State University.