CS 460 Machine Learning • 5 Cr.


This course is an introduction to the fundamentals and applications of machine learning. The course provides students with the opportunity to have theoretical knowledge and practical experience on basic concepts of machine learning with programming assignments. The course focuses on fundamentals, not on providing mastery of specific commercially available tools. Prerequisites: CS 300 with a C or better, MATH 208, MATH 270 and admission to BS CS program, or instructor's permission.


After completing this class, students should be able to:

  • List the differences among the three main styles of learning: supervised, reinforcement, and unsupervised 
  • Describe over-fitting in the context of a problem 
  • Implement simple algorithms for supervised learning, reinforcement learning, and unsupervised learning 
  • Develop an application that uses machine learning at its core 
  • Evaluate the performance of a simple learning system on a real-world dataset