Best Statistics and Machine Learning graduate certificate programs - Updated 2019

The Graduate Certificate in Statistics and Machine Learning is designed to formalize the training of students who contribute to or make use of statistics and machine learning as a significant part of their research. In addition, it serves to recognize the accomplishments of graduate students across the University who go beyond the requirements of their own degree programs to acquire additional training in statistics and machine learning. The certificate is administered by the Center for Statistics and Machine Learning and under the direction of the certificate director (currently, Prof. Peter Ramadge). The academic program coordinator for the Center for Statistics and Machine Learning provides the administrative support. The certificate program is open to Princeton University students currently enrolled in a Ph.D. or a master’s program at the University. Students may enroll by completing an online application form on the CSML website. The application includes a tentative plan and timeline for completing the requirements. We encourage eligible students to sign up as soon as possible, preferably in their second or third year, but no later than one semester prior to graduation. Because Ph.D. students who have entered Dissertation Completion Enrollment (DCE) status are not eligible to enroll in courses, Ph.D. students must enroll in the CSML graduate certificate program in time to complete the course requirements while they are still in their regular degree program length.

The Certificate in Data Mining and Machine Learning can be awarded in conjunction with any engineering master's degree. In order to qualify for this certificate, students enrolled in any master's in engineering program will need to meet all the requirements in addition to the standards requirements for their master's degree.

Cornell’s Machine Learning certificate program equips you to implement machine learning algorithms using Python. Using a combination of math and intuition, you will practice framing machine learning problems and construct a mental model to understand how data scientists approach these problems programmatically. Through investigation and implementation of k-nearest neighbors, naive Bayes, regression trees, and others, you’ll explore a variety of machine learning algorithms and practice selecting the best model, considering key principles of how to implement those models effectively. You will also have an opportunity to implement algorithms on live data while practicing debugging and improving models through approaches such as ensemble methods and support vector machines. Finally, the coursework will explore the inner workings of neural networks and how to construct and adapt neural networks for various types of data.

University of California-San Diego Specialized Certificate in Machine Learning Methods will introduce students to the subject matter using a variety of tools including: Linear Algebra, Deep Learning, TensorFlow, and Deep Neural Networks (DNN). A student completing this certificate will accumulate an important set of skills needed in current machine learning careers. Courses consist of both theoretical and practical skills complementing each other and hence enhancing the machine learning scientist abilities. Further courses are designed and classified to train students further in the field of machine learning, as well as coordinating with other engineering disciplines and industrial practices. Other Courses of Interest Not required for certificate. You may enroll in the certificate program at any time. However, it is recommended that you enroll as soon as possible. The program curriculum may be updated at any time if certificate requirements change, you must adhere to the curriculum at the time of your enrollment into the certificate. Candidates are encouraged to in the certificate program as early as possible to take advantage of program benefits.