MIT Professional Certificate Program in
Machine Learning & Artificial Intelligence

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ABOUT MIT PROFESSIONAL EDUCATION

For 70 years, MIT Professional Education has been providing technical professionals worldwide a gateway to renowned MIT research, knowledge and expertise, through advanced education programs designed specifically for them. In addition to industry-focused, two-to-five-day live virtual and on-campus courses through Short Programs, MIT Professional Education offers professionals the opportunity to take online and blended learning courses through Digital Plus Programs, attend courses abroad through International Programs, enroll in regular MIT academic courses through the Advanced Study Program, or attend Custom Programs designed specifically for their companies. For more information, please visit professional.mit.edu.

Equip yourself to succeed in the AI-powered future.

Master the AI tools, techniques, and algorithms reshaping the digital landscape. 


Develop data analysis skills needed to overcome complex machine learning challenges.

UPCOMING COURSES

Modeling and Optimization for Machine Learning

June 21 - 25, 2021 

Course Fee: $4,700

Faculty: Justin Solomon, Suvrit Sra


Master the data tools you need—from numerical linear algebra to convex programming—to make smarter decisions and drive enhanced results.

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Designing Efficient Deep Learning Systems

June 28 - 29, 2021

Course Fee: $2,500

 Faculty: Vivienne Sze


Discover how to build and utilize deep learning systems that extract meaningful information from large amounts of data.

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Deep Learning for AI and Computer Vision

January 25 - 29, 2021

Course Fee: $5,500

 Faculty: Antonio Torralba, Phillip Isola

Acquire the skills you need to build advanced computer vision applications featuring innovative developments in neural network research.

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Machine Learning for Big Data and Text Processing: Advanced

January 27 - 29, 2021

Course Fee: $3,500

Faculty: Regina Barzilay, Tommi Jaakkola, Stefanie Jegelka

Examine how the latest tools, techniques, and algorithms driving modern and predictive analysis can be applied to produce powerful results, even when using unstructured data. 

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Machine Learning for Big Data and Text Processing: Foundations

January 25 - 26, 2021

Course Fee: $2,500

Faculty: Regina Barzilay, Tommi Jaakkola, Stefanie Jegelka

Acquire the fundamental machine learning expertise you need to immediately implement new strategies for driving value in your organization. 

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PREPARE FOR SUCCESS IN THE DATA-POWERED FUTURE

Algorithmic bias. Dirty data. Customization. As the world generates and collects increasing amounts of data, the future belongs to those who can overcome today’s most pressing machine learning challenges. Across nearly every industry, demand has risen for data professionals who can study product or customer statistics, behaviors, and language and create predictive models of future behavior. Our MIT Professional Education machine learning certificate can help you keep up with this shifting landscape and identify new opportunities for growth and profit.

Created for anyone who works with data analysis and predictive modeling, the courses in this certificate will help you master the concepts, formulations, models, and algorithms fundamental to machine learning, and overcome the challenges facing you or your organization.


EXPERIENTIAL PROGRAMMING. INDIVIDUALIZED INSTRUCTION. CASE STUDIES.

Going far beyond lectures and discussions, these courses explore the outer limits of machine learning, demonstrating where it can be credibly applied, what tasks are still beyond its capabilities, and how it creates products that were recently unimaginable.

Equip yourself to succeed in the AI-powered future:

Explore the latest data-driven approaches "computer vision, deep learning, augmented reality, reinforcement learning."  

• Develop data analysis skills you need to overcome complex machine learning challenges.

• Master AI tools, techniques, and algorithms that are reshaping the digital landscape. 


Regina Barzilay is a Delta Electronics professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology.

Stefanie Jegelka is an X-Consortium Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT, where she is a member of CSAIL, and affiliated with IDSS.

Tommi Jaakkola is a professor of Electrical Engineering and Computer Science and also a member of the Computer Science and Artificial Intelligence Laboratory.

THE DETAILS

Awarded upon successful completion of 16 or more days of qualifying Short Programs courses, this certificate equips you with the best practices and actionable knowledge needed to put you and your organization at the forefront of the AI revolution.

How to Apply

To apply, submit an application for the program using the link below, along with a non-refundable $325 application fee. After you have been accepted into the program, you then apply for the individual courses that you intend to take this year.

Earn this prestigious professional credential by completing 16 or more days of qualifying Short Programs courses within a 36-month period. Start by submitting your application, along with a non-refundable $325 application fee. 

Explore the latest data-driven approaches "computer vision, deep learning, augmented reality, reinforcement learning."  

REGISTER NOW

Core Courses (5 Days)

Machine Learning for Big Data and Text Processing: Foundations⁠ — $2,500 (2 days)

June 07 - 08, 2021

Led by Regina Barzilay, Tommi Jaakkola, Stefanie Jegelka

Machine Learning for Big Data and Text Processing: Advanced ⁠— $3,500 (3 days)

June 09 - 11, 2021

Led by Regina Barzilay, Tommi Jaakkola, Stefanie Jegelka

Elective Courses (11 Days)

Ethics of AI: Safeguarding Humanity — $3,200 (3 days)

June 28 - 30, 2021

Led by Bernhardt L. Trout, Stefanie Jegelka

Modeling and Optimization for Machine Learning — $4,700 (5 days)

June 21 - 25, 2021

Led by Justin Soloman, Suvrit Sra

Computational Design for AI in Manufacturing — $5,500 (5 days)

July 12 - 16, 2021

Led by Wojciech Matusik

Machine Learning for Healthcare — $2,500 (2 days)

June 14 - 15, 2021

Led by David Sontag

Reinforcement Learning — $3,000 (3 days)

January 20 - February 19, 2021

Led by Pulkit Agrawal, Cathy Wu

Deep Learning for AI and Computer Vision — $5,550 (5 days)

July 19 - 23, 2021

Led by Antonio Torralba, Phillip Isola

Advances in Imaging and Machine Learning: Medical, VR-AR, and Self-Driving Cars — $2,500 (2 days)

Led by Ramesh Raskar

Bioprocess Data Analytics and Machine Learning — $3,500 (3 days)

June 28 - 30, 2021

Led by Richard D. Braatz, Brian Anthony, Seongkyu Yoon

Designing Efficient Deep Learning Systems — $2,500 (2 days)

June 28 - 29, 2021

Led by Vivienne Sze

Engineering Leadership in the Age of AI — $5,295 (5 days)

Led by David Martinez, David Niño

Foundations of Data and Models: Regression Analysis
 — $4,500 (5 days)

Jul 19 - 23, 2021

Led by Frank Dale Morgan

Applied Deep Learning Boot Camp — $2,500 (2 days)

Led by Regina Barzilay, Tommi Jaakkola

Questions? Speak to a program consultant
Contact Short Programs at 
shortprograms@mit.edu.

APPLY NOW

Earn this prestigious professional credential by completing 16 or more days of qualifying Short Programs courses within a 36-month period. Start by submitting your application, along with a non-refundable $325 application fee. 

Phillip Isola is an assistant professor in the MIT Department of Electrical Engineering and Computer Science. He received a bachelor’s degree in computer science from Yale University and a PhD in brain and cognitive sciences from MIT.

Ramesh Raskar is an Associate Professor at the MIT Media Lab and heads the Lab’s Camera Culture research group.

Justin Solomon is an Associate Professor of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, where he leads the Geometric Data Processing Group.

David Sontag joined the MIT faculty in 2017 as Hermann L. F. von Helmholtz Career Development Professor in the Institute for Medical Engineering and Science (IMES) and as Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS).

Suvrit Sra is an Associate Professor in the EECS department at MIT. He is also a core faculty member of the Institute for Data Systems and Society (IDSS) and PI in the Laboratory for Information and Decision Systems (LIDS).

Antonio Torralba is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), the MIT director of the MIT-IBM Watson AI Lab, and the inaugural director of the MIT Quest for Intelligence, a MIT campus-wide initiative to discover the foundations of intelligence.

Vivienne Sze received the B.A.Sc. (Hons) degree in electrical engineering from the University of Toronto, Toronto, ON, Canada, in 2004, and the S.M. and Ph.D. degree in electrical engineering from the Massachusetts Institute of Technology (MIT), Cambridge, MA, in 2006 and 2010 respectively.

Bernhardt L. Trout is the Raymond F. Baddour, ScD, (1949) Professor of Chemical Engineering at MIT. He received his S.B. and S.M. degrees from MIT and his Ph.D. from the University of California at Berkeley.

David R. Martinez is associate head in the Cyber Security and Information Sciences Division at MIT Lincoln Laboratory.

Wojciech Matusik is an Associate Professor of Electrical Engineering and Computer Science at the Computer Science and Artificial Intelligence Laboratory at MIT, where he leads the Computational Fabrication Group.

“Being part of the MIT ecosystem has put me ahead of the curve by providing access to the latest information, tools, and methodologies. The faculty are very helpful and truly want to see participants succeed.”

- Renzo Zagni, Co-founder, Intelenz

“MIT equipped me with the knowledge that I needed to expand my participation in the machine learning and AI components of my industry.”

- Peter Gathua, Founder, PMG Associates LLC

“The MITPE faculty members have great insight into what's coming next in AI — and even what's coming after that."

-Rick Durham, Data Science and AI Architect, Americas Global Black Belt Team,
Microsoft

CERTIFICATE FACULTY