CS 123 - Introduction to Machine Learning

4.0 Credits
An exploration of machine learning models with a focus on identifying when each is best used and data ethics. Models discussed include supervised learning, unsupervised learning and classification.
Prerequisite CS 122 with a minimum grade of 2.0 or instructor permission.
Course-level Learning Objectives (CLOs)
Upon successful completion of this course, students will be able to:

Explore types of machine learning models including supervised andunsupervised learning, and classify when to use these general models.
Compare supervised learning models, and examine differences between regression andclassification models.
Formulate linear regression models, evaluate its assumptions, and interpret its applications.
Design classification models using Logistic Regression andDecision Trees, and compute their performance.
Recognize when to use unsupervised learning models and applyclustering algorithms such ask-means to group data.
Explore Data Ethics in Machine Learning using case studies.

Course Typically Offered