CIS533 - Learning with Linear Classifiers

Course Description

In this course, you are introduced to and implement the Perceptron algorithm, a linear classifier that was developed at Cornell in 1957. Through the exploration of linear and logistic regression, you will learn to estimate probabilities that remain true to the problem settings. By using gradient descent, we minimize loss functions. Ultimately, you will apply these skills to build a email spam classifier.

The following courses are required to be completed before taking this course:

• Problem-Solving with Machine Learning
• Estimating Probability Distributions

Faculty Author

Benefits to the Learner

• Apply linear machine learning algorithms to solve classification and regression problems
• Identify the applicability, assumptions, and limitations of linear classifiers
• Implement the Perceptron algorithm for linear classification
• Choose an appropriate loss function for a given data set
• Use Gradient Descent to minimize loss functions
• Build a linear classifier for email spam classification

Target Audience

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

Applies Towards the Following Certificates

• Machine Learning : Required Courses