MIT Professional Education’s No Code AI and Machine Learning: Building Data Science Solutions Program, with a curriculum developed and taught by MIT faculty, can help you craft effective business solutions without writing a single line of code.
This course is designed to help you learn and apply advanced data tools for IIoT and smart manufacturing. The curriculum ranges from foundational concepts to in-depth, hands-on activities using production data, and covers a variety of cutting-edge approaches such as deep reinforcement learning control, encryption for data outsourcing, and predictive data analytics algorithms.
Tim Kraska
Tim Kraska is an Associate Professor of Electrical Engineering and Computer Science in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), where he co-leads the Data Systems Group. He is also the Founding Co-Director of MIT Data System and AI Lab (DSAIL).
Julian Shun
Julian Shun is an Associate Professor of Electrical Engineering and Computer Science at MIT and a lead investigator in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on the theory and practice of parallel algorithms and programming, with particular emphasis on designing algorithms and frameworks for large-scale graph processing and spatial data analysis. He also studies parallel algorithms for text analytics, concurrent data structures, and methods for deterministic parallelism. Prior to joining MIT, he was a postdoctoral Miller Research Fellow at UC Berkeley. His honors include the NSF CAREER award, DOE Early Career Award, ACM Doctoral Dissertation Award, the CMU School of Computer Science Doctoral Dissertation Award, Google Faculty Research Award, Google Research Scholar Award, SoE Ruth and Joel Spira Award for Excellence in Teaching, Facebook Graduate Fellowship, and best paper awards at PLDI, SPAA, CGO, and DCC.
Graph analytics provides a valuable tool for modeling complex relationships and analyzing information. In this course, designed for technical professionals who work with large quantities of data, you will enhance your ability to extract useful insights from large and structured data sets to inform business decisions, accelerate scientific discoveries, increase business revenue, improve quality of service, detect fraudulent behavior, and/or defend against security threats.
La prise de décisions dicte l’orientation et le développement de chaque entreprise: ceux qui sont responsables de ces décisions doivent être en mesure de les prendre en toute confiance. Le machine learning devient un outil fondamental pour prendre des décisions éclairées en analysant d'importantes quantités de données et d'événements. Il a pour objectif de réduire les zones d'incertitude et de hasard grâce à l'apprentissage automatique et à une analyse efficace des données.
A fábrica do futuro já está aqui. Participe do programa online Manufatura Inteligente: Produção na Indústria 4.0 e aproveite a experiência de mais de cem anos de colaboração do MIT com vários setores. Aprenda as chaves para criar uma indústria inteligente em qualquer escala e saiba como software, sensores e sistemas são integrados para essa finalidade. Com este programa interativo, você passará da criação de modelos a sistemas de fabricação e análise avançada de dados para desenvolver estratégias que gerem uma vantagem competitiva.
John N. Tsitsiklis is a Clarence J Lebel Professor, with the Department of Electrical Engineering and Computer Science (EECS) at MIT and the Laboratory for Information and Decision Systems (LIDS).
Caroline Uhler is an assistant professor in EECS and IDSS at MIT. She holds an MSc in Mathematics, a BSc in Biology, and an MEd in High School Mathematics Education from the University of Zurich. She obtained her PhD in Statistics from UC Berkeley in 2011.
Her research focuses on mathematical statistics, in particular on graphical models and the use of optimization, algebraic and geometric methods in statistics, and on applications to biology.
Munther Dahleh is director of the Institute for Data, Systems, and Society. He was previously the associate department head of EECS. He is also a member of MIT’s Laboratory for Information and Decision Systems (LIDS).