Biologically-Inspired Artificial Intelligence

Catalog description

Prerequisite: at least one semester of programming experience in a high-level, object-oriented language such as Python, Java, or C++ The field of artificial intelligence (AI) is concerned with reproducing in computers the abilities of human intelligence. In recent years, exciting new approaches to AI have been developed, inspired by a wide range of biological processes and structures that are capable of self- organization, adaptation, and learning. These sources of inspiration include biological evolution, neurophysiology, and animal behavior. This course is an in-depth introduction to the algorithms and methodologies of biologically-inspired AI and is intended for students with prior programming experience. We will focus primarily on machine-learning techniques—including genetic algorithms, reinforcement learning, artificial neural networks, and deep learning—from both a theoretical and a practical perspective. Throughout the course, we will use the Python programming language to implement and experiment with these algorithms in detail. Students will have many opportunities for extended exploration through open-ended, hands-on lab exercises and conference work. Courses offered in related disciplines this year are listed below. Full descriptions of the courses may be found under the appropriate disciplines. Geospatial Data Analysis (p. 55), Bernice Rosenzweig Environmental Science Calculus I: The Study of Motion and Change (p. 105), Daniel King Mathematics An Introduction to Statistical Methods and Analysis (p. 105), Daniel King Mathematics Calculus II: Further Study of Motion and Change (p. 106), Melvin Irizarry-Gelpi Mathematics Multivariable Mathematics: Linear Algebra, Vector Calculus, and Differential Equations (p. 106), Bruce Alphenaar Mathematics First-Year Studies: Foundations of Modern Physics (p. 11), Sarah Racz Physics Foundations of Modern Physics (p.…