Catalog Description:
Techniques for the design and analysis of algorithms. Case studies of
existing algorithms (sorting, searching, graph algorithms, dynamic
programming, matrix multiplication, fast Fourier
transform). NP-Completeness.
Goal:
The goal of this course is to give students an introduction to the
design, analysis and implementation of algorithms at the undergraduate
level. The following topics will include: mathematical foundations,
some elementary data structures, sorting and searching algorithms,
graph algorithms, algorithmic design paradigms, and an introduction to
NP-completeness. If time permits, we will cover some elementary
computational geometry algorithms.
The goal of this course is to give students a familiarity with basic techniques of artificial intelligence as well as an appreciation of the history of the development of the techniques. Some of the basic topics to be covered include: history of AI, problem-solving and search, knowledge representation, automated reasoning, learning and reasoning under uncertainty, neural networks, genetic algorithms, reinforcement learning, natural-language processing, computer vision, robotics, and various philosophies of AI.
The goal of this course is to teach the principles of computer game design for computer scientists and to elucidate the important role that AI techniques play in game design and development. Although computer game design is not a part of the traditional computer science curriculum, computer games have come to play a significant role in education, training, modeling, and research into human cognition. As a result it has become critical that computer scientists have some exposure to the methodologies and techniques that utilize advanced computing technologies. In this course the student will learn the basic principles of computer game design, the most popular techniques and technologies for game implementation, and the many ways in which advances in computer graphics and artificial intelligence influence game design. The course will consist of lectures and laboratory work. Students will have to complete required readings, homework assignments, a midterm exam, and a term project consisting of the design and implementation of an AI for a computer game. Each student will be required to write a final project report and give a presentation on their work.
Prereq: CS 441: Artifical Intelligence; STAT 451 Applied Statistics for Scientists and Engineers; Programming experience in C, C++, Java or Lisp.
Catalog Description:
Introduction to the theory and practice of machine learning with emphasis on fundamental techniques, including concept learning, decision tree methods, artificial neural networks, Bayesian learning, genetic algorithms, and reinforcement learning.
Goal:
The goal of this course is to give students an introduction to the theory and
practice of machine learning. The course will include the following topics:
hypothesis estimation and statistical sampling theory, concept learning,
decision tree learning, artificial neural networks, Bayesian learning,
instance learning, genetic algorithms, and reinforcement learning.
Prereq: CS 441: Artifical Intelligence; CS 447, 448: Computer GRaphics I, II (desired); Programming experience in C, C++, Java or Lisp; Experience with Visual Basic is helpful. Permission of the Instructor.
Catalog Description:Prereq: CS 441: Artifical Intelligence; Programming experience in C, C++, Java or Lisp; Permission of the Instructor.
Catalog Description: