NEURAL NETWORKS in machine learning
Johns Hopkins University Certificate of CompletionCurriculum designed and delivered by JHU FacultyStart the course anytime through open enrollmentLIVE monthly seminars with JHU FacultyEngaging learning including video lectures and hands-on activities
Course opens on February 3, 2025. Investment: $500
4 Weeks • 20 Hours • Online
• Hands-on Projects • $500
Earn a certificate of completion from JHU, a recognized leader in education and research. Earn 2 Continuing Education Units (CEUs) upon program completion.
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Neural Networks provides a deep dive into neural network architectures and their applications. This course covers linear models, linear networks, feedforward neural networks, and alternative neural network structures. Students will gain hands-on experience with neural network techniques and understand their use in solving complex problems.
The course is for professionals looking to advance their expertise in neural network architectures, including linear models, feedforward networks, and alternative neural structures, with hands-on experience solving complex problems.
AI and ML PractitionersData ScientistsTechnical Leads and ManagersComputer EngineersSoftware DevelopersDesigned by JHU Faculty, this course delves into advanced decision tree learning techniques, equipping learners with the skills to tackle complex classification and regression challenges. Key skills include:
Introduction to Linear Models
Linear Networks in Machine Learning
Feedforward Neural Network Design and Training
Convolutional and Recurrent Networks
Ensembles in Neural Networks
Dimensionality Reduction Techniques in Deep Learning
Enhance your learning experience through monthly live seminars designed to deepen your expertise and allow you to engage directly with faculty experts at the forefront of their fields, gaining valuable insights, practical advice, and real-world perspectives.
Johns Hopkins University faculty are readily accessible to address your questions directly through the course platform, ensuring personalized guidance and a seamless learning experience.
Learn at your own pace with engaging and helpful learning materials such as video lectures, relevant readings, and case studies.
Hands-on activities that help you demonstrate competence and enabling reskilling and upskilling.
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.
4 Weeks • Online • Hands-on Projects
4 Weeks • Online • Hands-on Projects
12 Weeks • Online • Hands-on Projects
4 Weeks • Online • Hands-on Projects • $500
Submit your details below to learn more about the course curriculum, benefits, fee and more.
Submit your details below to learn more about the course curriculum, benefits, fee and more.
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