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.
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: