A tomada de decisão condiciona a evolução de qualquer empresa e seus responsáveis devem ser capazes de decidir da maneira segura, eliminando a casualidade do processo. O Machine Learning já é uma ferramenta fundamental para a tomada de decisões assertiva, possibilitando a análise de grandes quantidades de dados e eventos. Seu objetivo é reduzir espaços de incerteza e arbitrariedade por meio de aprendizado automático e análise eficiente de dados.
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.
La fábrica del futuro ya está aquí:
Toma ventaja de los cien años de trayectoria que tiene MIT en colaboración con varias industrias para entrar al programa online Smart Manufacturing: Operaciones en la 4ª Revolución Industrial.
Dr. Carmine Gioia is a Research Affiliate and Lecturer in the Department of Civil and Environmental Engineering at MIT’s Intelligent Transportation Systems Lab. He holds a Ph.D. in Microeconometrics Methods and has a unique combination of industry and academic experience.
Cathy Wu is the Gilbert W. Winslow Career Development Assistant Professor of civil and environmental engineering at MIT and has worked across many fields and organizations, including Microsoft Research, OpenAI, the Google X Self-Driving Car Team, AT&T, Caltrans, Facebook, and Dropbox. Wu is also the founder and Chair of the Interdisciplinary Research Initiative at the ACM Future of Computing Academy.
Pulkit Agrawal
Participating Instructor
Pulkit Agrawal is assistant professor of electrical engineering and computer science at MIT and leads the Improbable AI Lab, part of the Computer Science and Artificial Intelligence Lab at MIT and affiliated with the Laboratory for Information and Decision Systems. Agrawal also co-founded SafelyYou, an organization that builds fall prevention technology, and the AI Foundry, an incubator for AI startups.
Dr. Seongkyu Yoon is a Professor in the Department of Chemical Engineering and the Ward Endowed Professor in Biomedical Sciences at UMass Lowell. He is also the UMass Site Director of the Advanced Mammalian Biomanufacturing Innovation Center and a contributor to the National Biomanufacturing Innovation Institute. Dr. Yoon runs a systems biology group that conducts research on systems biotechnology, life science informatics, bioprocess data analytics, and regulatory sciences with the objective of developing innovative biomanufacturing platforms for protein/cell/gene biotherapeutics.
Richard D. Braatz
Participating Instructor
Dr. Richard D. Braatz is the Edwin R. Gilliland Professor of Chemical Engineering at MIT, where he conducts research into advanced biopharmaceutical manufacturing systems. In this role, he leads process data analytics, mechanistic modeling, and control systems for several projects on campus, including those focused on monoclonal antibody, viral vaccine, and gene therapy manufacturing.
Get more from your bioprocess data. In this intensive, four-day course, designed specifically for scientists and engineers in the biopharma industry, you’ll explore best practices for translating biopharmaceutical manufacturing data into reliable models and better decisions. Working with academic and industry experts, you’ll acquire strategies for improving manufacturing accuracy, enhancing regulatory efficiency, and refining bioprocess operations.
La toma de decisiones condiciona la evolución de todas las empresas y los responsables deben ser capaces de elegir opciones de la forma más segura posible, después de eliminar el azar en el proceso. El machine learning, una vertiente de la inteligencia artificial, ha nacido para responder a esa necesidad, y se ha convirtiendo en una herramienta fundamental para la toma de decisiones fiables a través del análisis automatizado de grandes cantidades de datos y hechos.