Principles and Methods of Machine Learning

The image is for illustrative purposes only. The actual certificate may be subject to change at the discretion of Johns Hopkins University

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  • Real-World Application: Practice and refine your skills with practical projects in every course.
  • Expert Guidance: Learn from Johns Hopkins University instructors and industry professionals.
  • Flexible Online Format: Designed for busy professionals, offering the convenience to learn on your schedule.
  • All courses in the certificate program
  • Certificate program capstone project.
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Johns Hopkins University Instructor

Dr. Sheppard is a Norm Asbjornson College of Engineering Distinguished Professor in the Gianforte School of Computing at Montana State University and is a former Adjunct Professor in the Computer Science Department at Johns Hopkins. His research interests include model-based and Bayesian reasoning, reinforcement learning, game theory, and fault diagnosis/prognosis of complex systems. He is a Fellow of the IEEE, elected “for contributions to system-level diagnosis and prognosis.”

Dr. Sheppard received his BS in computer science from Southern Methodist University in 1983. Later, while a full-time member of industry, he received an MS in computer science in what is now Johns Hopkins Engineering for Professionals (1990). He continued his studies and received his Ph.D. in computer science from Johns Hopkins in the day school (1997), completing a dissertation on multi-agent reinforcement learning and Markov games.

Prior to entering academia full time, Dr. Sheppard was a member of industry for 20 years. His prior position was as a research fellow at ARINC Incorporated. Dr. Sheppard became a member of the EP faculty in 1994 where he teaches courses in machine learning and population-based algorithms. He also mentors independent studies and advises several graduate students. In 2022, he received the Provost’s Award for Graduate Research and Creativity Mentoring at Montana State University, which recognizes excellence in advising MS and PhD students.

10 Weeks • Online • Hands-on Projects