CS 467 - Introduction to Machine Learning

Units: 3
Intro to machine learning concepts and algorithms, supervised learning for classification and regression, unsupervised learning, reinforcement learning. Topics such as VC dimension, central limit theorem, gradient descent, SVM, deep neural networks, GANs, generative AI.

Prerequisites: CS 401 or CS 413.
Possible Instructional Methods: On-ground, or Hybrid, or Online-Asynchronous, or Online-Synchronous.
Grading: A-F grading only.
Course Typically Offered: Fall & Spring

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

Author creative and robust programs to solve real-world problems.
Apply machine learning paradigms.
Utilize supervised/unsupervised learning, and reinforcement learning techniques.
Apply neural networks and basics of machine learning to real-world problems.