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CS 345 - Machine Learning Foundations and Practice

  • 3 credits
View available sections

Machine learning algorithms and tools for predictive modeling presented using case studies that inform their use in real-world applications. Credit not allowed for both CS 345 and DSCI 445 (Statistical Machine Learning).

Prerequisite

CS 220 with a C or better; CS 150B with a C or better or CS 152 with a C or better or CS 165 with a C or better or DSCI 235 with a C or better; MATH 155 with a C or better or MATH 159 with a C or better or MATH 160 with a C or better; STAT 301 with a C or better or ECE 303/STAT 303 with a C or better or STAT 307 with a C or better or STAT 315 with a C or better.

Instructors

Charles Anderson
Charles Anderson

9704917491 | chuck.anderson@colostate.edu

Dr. Charles W. Anderson is a professor of Computer Science at CSU. He graduated with a Ph.D. in computer science from the University of Massachusetts, Amherst, in 1986, and worked at GTE Laboratories in Waltham, MA, until arriving at CSU in 1991. Dr. Anderson works with neural networks, reinforcement learning, EEG pattern recognition, neural modeling, HVAC control, adaptive tutoring, computer graphics, computer vision, and software and hardware testing.