Gain a core understanding of the management approaches of the future. In this high-impact three-day course, featuring lectures, group exercises, and discussions, you’ll master specialized skills in people analytics, management, and ethics—and emerge ready to lead with impact across your enterprise.
Andrew Lawrie is an analyst studying geometric PDEs. His research focuses on the asymptotic dynamics of solutions to various geometric dispersive equations. These equations often arise as models in mathematical physics, and their study brings together techniques from harmonic analysis, nonlinear analysis, and geometry.
Take a deep dive into the mathematical foundations of AI and machine learning. You’ll explore the math behind not only fundamental models and algorithms, but also recent innovations such as Transformers and Graph Neural Nets—and discover how these concepts relate to Python code and associated applications.
Autonomous robots. Self-driving cars. Smart refrigerators. Now embedded in countless applications, deep learning provides unparalleled accuracy relative to previous AI approaches. Yet, cutting through computational complexity and developing custom hardware to support deep learning can prove challenging for many enterprises—and the cost of getting it wrong can be catastrophic. Do you have the advanced knowledge you need to keep pace in the deep learning revolution?
Over the past eight years, the amount of computing required to run these neural nets has increased over a hundred thousand times, which has become a significant challenge. Gain a deeper understanding of key design considerations for deep learning systems deployed in your hardware.
Fuel your organization’s ability to produce large volumes of highly integrated, complex, customized products by leveraging intelligent design and manufacturing strategies powered by the latest in artificial intelligence. In this highly interactive course, you’ll join a group of accomplished global peers to explore the latest smart manufacturing strategies and hardware, acquire skills to develop machine learning-based design templates, and participate in generative design sessions.
An active area of research, reinforcement learning has already achieved impressive results in solving complex games and a variety of real-world problems. However, organizations that attempt to leverage these strategies often encounter practical industry constraints. In this dynamic course, you will explore the cutting-edge of RL research, and enhance your ability to identify the correct approach for applying advanced frameworks to pressing industry challenges.
Reinforcement learning (RL), is enabling exciting advancements in self-driving vehicles, natural language processing, automated supply chain management, financial investment software, and more. In this three-day course, you will acquire the theoretical frameworks and practical tools you need to use RL to solve big problems for your organization
Motors are becoming better and cheaper—opening profitable new applications across industries. In this course for engineers and product designers, you will learn to assess and design electric motors, generators, and drive systems, with emphasis on electric drives, including traction drives and drive motors. You will also explore how modern embedded controllers enable command through digital computation, breathing life into electric machines and motion control applications.
Katie Nelson
Katie has been the Social Media and Public Relations Coordinator at the Mountain View Police Department since 2015. In her role, she manages the department’s social media accounts and outreach campaigns, focusing primarily on the department’s engagement efforts as well as serving as the agency’s public information officer. She presents across the country on social media best practices and crisis communication strategies.
Anna Frebel is an observational astronomer and astrophysicist. Prof. Frebel has authored more than 120 papers in various refereed journals and is best known for her discoveries and spectroscopic analyses of the oldest, most metal-poor stars in the Milky Way and small dwarf galaxies.