CIS536 - Learning with Kernel Machines

Course Description

In this course, you will explore support-vector machines and use them to find a maximum margin classifier. You will then construct a mental model for how loss functions and regularizers are used to minimize risk and improve generalization of a learning model. Through the use of feature expansion, you will extend the capabilities of linear classifiers to find non-linear classification boundaries. Finally, you will employ kernel machines to train algorithms that can learn in infinite dimensional feature spaces.

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
• Debugging and Improving Machine Learning Models

Faculty Author

Benefits to the Learner

• Find a maximum margin classifier using support-vector machines
• Organize the landscape of machine learning algorithms into a unified framework
• Identify the right regularizer for a given problem
• Make linear classifiers non-linear through implicit and explicit feature expansion
• Manipulate and utilize kernels to train algorithms in infinite dimensional feature spaces

Target Audience

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

Applies Towards the Following Certificates

• Machine Learning : Required Courses