machine learning course
Machine Learning in Business
Course starts September 23, 2026

Machine learning (the science of programming computer systems to learn from data), offers an opportunity to gain a powerful competitive edge in the business market, and is increasingly becoming a priority for managers and executives.

In this online course from the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), you’ll be guided to discover the business potential of machine learning, while developing strategies for effective implementation.

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STL Lecture April 2019
SystemsThatLearn@CSAIL Lecture Series: Applied Machine Learning at Scale at Salesforce

Access the recording and presentation from the lecture under "Agenda".

Abstract:
Phil and his Salesforce colleagues, Sonke Rohde and Steven Tamm, shared cutting-edge ML research as applied to hyper-personalized commerce and enterprise systems. They peeled under the cover of the successful Salesforce "platform" as a triumph of the monolith as a system engineering pattern. He provided an honest assessment of how a silicon valley company approaches innovation while balancing customer demands and diverse ethical concerns around the world.

Hello World, Hello MIT
MIT news article
Hello World, Hello MIT: College of Computing Launch

Artificial intelligence and the evolving domains of computer science will be defining forces in the next phase of human history. Our shared future hinges on the responsible and ethical evolution of technologies that are transforming every aspect of modern life.

This is how MIT will shape the future.

Computing the Future- Turing Panel
MIT news article
Computing the Future

Fireside chat brings together six Turing Award winners to reflect on their field and the MIT Stephen A. Schwarzman College of Computing.

AI research
CSAIL article
MIT CSAIL Holds Trustworthy AI Event with Microsoft

MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) held a special workshop with Microsoft Research to explore key challenges in creating trustworthy and robust artificial intelligence (AI) systems. The effort focused on addressing concerns about the trustworthiness of AI systems, including rising concerns with the safety, fairness, and transparency of the technologies.

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