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:

Describe the paradigms of supervised and unsupervised machine learning.
Explain the fundamental issues and challenges of machine learning.
Identify the strengths and weaknesses of multiple machine learning approaches.
Formalize a task as a machine learning problem.
Identify suitable algorithms to address different machine learning problems.
Utilize machine learning frameworks for practical problem solutions.
Create cloud computing machine learning models.