CS 223 - Supervised Learning Models

2.0 Credits
Compare and apply supervised machine learning models (such as classification models versusregression models) using popular modeling techniques including linear regression, decision trees, random forests etc.This course is part of the Data Analytics for Professionals program.
Prerequisite Successful completion of CS 222or instructor permission.
Course-level Learning Objectives (CLOs)
Upon successful completion of this course, students will be able to:

Review linear regression, pitfalls and how to compare predicted values to actuals.
Compare and contrast techniques to apply when datasets are small or when overfitting might occur such as cross-validation, bootstrapping, and bagging.
Use supervised models for numerical data prediction using decision trees, random forest, neural networks and gradient boosting trees.
Evaluate regression models with error measurements.
Classify categorical outputs using tree-based models, logistic regression, Naive Bayes and Linear SVM models.
Evaluate classification models using confusion matrices, plotting ROC, precision and recall curves, and calculating other performance metrics

Course Typically Offered
Winter