CSC 115 Machine Learning I

Lecture: 2 Lab: 3 Clinic: 0 Credits: 3

This course covers algorithms for enabling artificial systems. Topics include machine learning from experience, supervised and unsupervised learning, reinforcement learning control, and learning theory. Upon completion, students should be able to demonstrate machine-learning techniques.

Course is typically offered in Spring.
Course has transfer restrictions - 10 years

Student Learning Outcomes (SLOs)
Students will develop competencies with SLOs presented below upon completion of the class:

Compare supervised and unsupervised machine learning approaches.
Analyze the strengths and weaknesses of machine learning approaches.
Experiment with machine learning approaches for practical problem solutions.
Assess ethical considerations when implementing machine learning concepts and techniques.