CS 667 - Machine Learning

Units: 3
Introduction to machine learning with an emphasis on the underlying mathematics. Topics such as VC dimension, central limit theorem, gradient descent. Applications such as SVM and neural nets.

Prerequisites: MATH 230 and MATH 310; or M.S. Computer Science major.
Possible Instructional Methods: Entirely On-ground, or Hybrid.
Grading: A-F grading only.
Course Typically Offered: Fall & Spring

Student Learning Outcomes - Upon successful completion of this course students will be able to:

Demonstrate proficiency in the concepts, techniques, and applications of machine learning
Implement in code a variety of machine learning algorithms
Understand the factors and tradeoffs involved when choosing between different machine learning approaches
Apply machine learning algorithms to novel, real-world problems
Identify the relevant ethical and social considerations inherent to machine learning practices