CIS535 - Debugging and Improving Machine Learning Models

Course Description

In this course, you will investigate the underlying mechanics of a machine learning algorithm’s prediction accuracy by exploring the bias variance trade-off. You will identify the causes of prediction error by recognizing high bias and variance while learning techniques to reduce the negative impacts these errors have on learning models. Working with ensemble methods, you will implement techniques that improve the results of your predictive models, creating more reliable and efficient algorithms.

These courses are required to be completed prior to starting this course:

• Problem-Solving with Machine Learning
• Estimating Probability Distributions
• Learning with Linear Classifiers
• Decision Trees and Model Selection

Faculty Author

Benefits to the Learner

• Identify the cause of high prediction error by recognizing high bias or high variance
• Mitigate the negative impact of a bad bias/variance trade-off on your model
• Analyze how ensemble methods reduce bias and variance in order to improve the predictive model
• Implement bagging and boosting to improve the predictive model

Target Audience

• Programmers
• Developers
• Data analysts
• Statisticians
• Data scientists
• Software engineers

Applies Towards the Following Certificates

• Machine Learning : Required Courses