Loading...

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

Kilian Weinberger

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

Loading...
Enroll Now - Select a section to enroll in
Type
2 week
Dates
Nov 05, 2025 to Nov 18, 2025
Total Number of Hours
18.0
Course Fee(s)
Regular Price $1,199.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note the content in the Machine Learning course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order. For example; CIS531 prior to CIS532, CIS532 prior to CIS533, etc.

  • This program also includes two additional self-paced linear algebra courses to assist you in your coursework. You will be automatically enrolled in the courses and can access them at any time before or during your Machine Learning program. If you need to refresh your linear algebra skills, it is highly recommended that you access these additional resources prior to the start of your first course.

Type
2 week
Dates
Dec 03, 2025 to Dec 16, 2025
Total Number of Hours
18.0
Course Fee(s)
Regular Price $1,199.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note the content in the Machine Learning course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order. For example; CIS531 prior to CIS532, CIS532 prior to CIS533, etc.

  • This program also includes two additional self-paced linear algebra courses to assist you in your coursework. You will be automatically enrolled in the courses and can access them at any time before or during your Machine Learning program. If you need to refresh your linear algebra skills, it is highly recommended that you access these additional resources prior to the start of your first course.

Type
2 week
Dates
Dec 31, 2025 to Jan 13, 2026
Total Number of Hours
18.0
Course Fee(s)
Regular Price $1,199.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note the content in the Machine Learning course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order. For example; CIS531 prior to CIS532, CIS532 prior to CIS533, etc.

  • This program also includes two additional self-paced linear algebra courses to assist you in your coursework. You will be automatically enrolled in the courses and can access them at any time before or during your Machine Learning program. If you need to refresh your linear algebra skills, it is highly recommended that you access these additional resources prior to the start of your first course.

Required fields are indicated by .