TEACHING
FALL 2020 at Clemson (Online)
CSPC 4430/6340: Machine Learning: Implementation and Evaluation
Students learn to code machine learning algorithms from basic principles, without machine learning libraries. Topics include supervised learning such as regression and classification; unsupervised learning approaches such as clustering; and measures of performance such as bias/variance theory, measures, and error metrics.
CPSC 2070: Discrete Structures for Computing
Introduces ideas and techniques from discrete structures that are widely used in the computing sciences. Topics emphasize techniques of rigorous argumentation and application to the computing disciplines.
OTHER COURSES I HAVE TAUGHT AT CLEMSON (2008-2020)
  • Solving Unstructured Problems
  • Computational Thinking with Python
  • New PhD Student Seminar
  • Measurement and Evaluation of Human-Centerd Computing Systems (Co-taught)
COURSES TAUGHT AT UNC-CHARLOTTE (2002-2008)
  • Introduction to Programming II
  • Operating Systems
  • Virtual Environment
  • Logic and Algorithms
  • PhD Student Seminar
COURSES TAUGHT AT GEORGIA TECH (1988-2002)
  • Understanding and Constructing Proofs
  • Data Structures
  • Introduction to Computer Graphics
  • Advanced Techniques in Computer Graphics
  • Visualization Techniques in Science and Engineering 
  • Stereoscopic Computer Graphics (Seminar course)
  • Virtual Environments
COURSES TAUGHT AT NORTH CAROLINA STATE (1978-1988)
  • Algebra and Trigonometry
  • Calculus I
  • Busniess Calculus
  • Introduction to FORTRAN Programming
  • Computer Organization and Assembly Language
  • Operating Systems
  •  Data Structures
  •  Numerical Methods
  •  Introduction to Computer Graphics
  • Advanced Computer Graphics