Teach Machines to Teach Themselves
Next start date
October 5, 2017
Machine learning is changing our world in profound and fundamental ways. We can already see the results in innovations such as customized online recommendations, speech recognition, predictive policing and fraud detection. Future applications are limited only by the imagination.
In this three-course certificate program, we’ll examine all aspects of machine learning. You’ll study the probability concepts and statistical methods that are at the core of machine learning algorithms. We’ll also practice ways to apply these techniques, using open-source tools — along with your developing judgment and intuition — to address actual business needs and real-world challenges.
Software developers, experienced data analysts and other technical professionals who want to become machine learning specialists.
You may be asked to take an assessment quiz and/or consult with the program manager to see if you meet admission requirements.
If you're not a native English speaker, you’ll need to have at least basic English language skills to enroll. To learn more, see English Language Proficiency Requirements.
International students are welcome to apply to an online offering of this program, which doesn’t require a visa. To enroll in a classroom offering, you must have a visa that permits study in the United States. This program does not enable students to obtain or maintain F-1 visa status. For more information, see Admission Requirements for International Students.
You must have access to a computer, and we recommend a high-speed internet connection.
You earn the certificate by successfully completing all required courses. For more information, see Completing Your Program.
You may be eligible to apply for a UWPCE Certificate Scholarship to cover the cost of this program. Scholarships are awarded based on financial need and the potential of the program to positively impact your career. See the scholarship page for eligibility information and application instructions.
Complete the courses listed below to earn the certificate. You may be able to take individual courses without enrolling in the certificate program; check the course pages for details.
Introduction to Machine Learning
Advanced Machine Learning
Applied Machine Learning
Certificate in Machine Learning
Approved by the UW Department of Computer Science & Engineering.
View this program's advisory board.
Each of our programs uses an advisory board to review content, guide design and recommend updates to ensure the program remains current as the field of study evolves. By tapping the minds of the top thinkers, doers and leaders in the field, we offer a transformational learning experience. The following individuals serve as the advisory board for this program.
Roger Barga, General Manager and Director of Development, Amazon Web Services
Rovy Branon, Vice Provost, University of Washington Continuum College
Lawrence Clayton, Data Scientist Lead, Context Relevant
David DeBarr, Principal Applied Researcher, Microsoft
Mike Friedman, Lead Software Engineer, Salesforce
Julia Letchner, Director of Data Science, Onvia
Ying Li, Chief Data Scientist, Jobaline.com
Piyu Roy, Program Manager, UW Professional & Continuing Education
Date: Tuesday, August 15, 2017
Time: 6:00 p.m.–6:45 p.m. Pacific Time
Date: Tuesday, October 24, 2017
Download the application form (PDF)
This program is full, but you may join the waitlist by submitting an application. If you’re not offered a spot in the program, you’ll have the chance defer your enrollment to the next offering.
Apply online, or submit an application packet that includes:
We’re currently accepting applications and will be reviewing them in the order they’re received. We’ll accept applications until the program closes.
We’ll contact you within two weeks of receiving your application materials to let you know if you’ve been accepted to the program.
If you’re accepted, we’ll send you details about your first-term course, including information on paying your course fee. Your course fee is due four weeks before the first class.
You may qualify for an income-based scholarship for this program. To learn about eligibility requirements and how to apply, visit the UWPCE Certificate Scholarship page
With the exception of the one-time application fee, certificate program fees are charged on a quarterly basis. Payment is due when you register.
Course fees do not include any costs for class materials such as textbooks and software.
Drops, Withdrawals & Refunds
Attend class in person and engage face to face with your classmates and instructors as part of a highly interactive curriculum. Assignments and other course materials are delivered through Canvas, a web-based learning management system that also serves as a communications hub. You must attend at least 60 percent of your class sessions in person. You may view the remainder online, either as recorded sessions or in real time (limit of two live-streamed sessions per quarter).
Courses are streamed online in real time from the classroom. You interact with your instructors and fellow students via chat, using Adobe Connect web conferencing software. Assignments and other course materials are delivered through Canvas, a web-based learning management system that also serves as a communications hub.For added flexibility, each quarter you may view recordings of up to 40 percent of your class sessions instead of attending in real time. You also can attend the classroom sessions in person if space is available.
Our special blended programs combine the convenience of online study with the vibrancy of in-person classes for a unique learning experience.
Not available for this program
Principal Applied Researcher, Microsoft
Lead Software Engineer, Salesforce
Vice President of Engineering and Analytics, Edifecs
If you’re thinking about heading back to school, you’re probably also wondering how you’ll pay for it. To help you figure that out, we’ve put together a list of ways to help fund your education.
Study distributed computing and the tools used to store and process data. Gain experience with the kinds of data flow situations commonly used to inform key business decisions.
Learn how to apply cutting-edge tools and techniques to extract meaning from data sets ranging in size from gigabytes to petabytes.
Explore the power and potential of natural language processing in a variety of fields, and learn to bridge the gap between people and machines using a variety of NLP applications.
Acquire the skills needed to create resilient, elastically scalable, responsive and distributed web applications for big-data environments.
Acquire the skills to perform sophisticated data analysis and modeling, data mining and big data management using powerful statistical tools and R programming.
Be at the forefront of scientific innovation by learning to transfer human competencies, such as language and communication, to computers.
Discover how to use scientific computing tools and technologies to help solve complex problems in the physical, biological and engineering sciences.