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Driving Innovation with Generative AI

Explore the generative AI landscape and the future of productivity with this 6-week course from MIT xPRO.

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  • START DATE February 10, 2025 More Dates
  • TIME COMMITMENT 4-7 hours per week
  • DURATION 6 Weeks
  • FORMAT Online
  • PRICE $2,979

WHAT YOU WILL LEARN

Generative AI is creating cutting-edge solutions that will revolutionize industries and empower businesses to thrive in the digital age. This six-week course leverages industry case studies, hands-on work with generative AI tools, and the latest thinking from 12 faculty members from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) to equip you with the knowledge and skills necessary to navigate the intricate world of generative AI.

View the week by week schedule here.

  • Develop a comprehensive understanding of the fundamentals and distinctions of Generative AI and machine learning such as neural networks and the role of data.
  • Acquire the skills to analyze and utilize image generative models, including comprehending their key components, inputs, and outputs, and critically assess the significance of latent variables and noise in their operational effectiveness.
  • Evaluate the evolution, limitations, and future possibilities of Large Language Models (LLMs), tracing their developmental history, understanding current constraints, and exploring potential advancements and applications in various fields.
  • Understand Generative AI in coding, design, and chemistry, mastering algorithms for molecular property prediction and code generation, and comprehending the fundamental architectures for molecular design, code creation, and 3D structural design.
  • Explore the collaborative efforts and connection between Generative AI and humans, focusing on creative expression and human emotion.
  • Recognize the essentials of Pro-Human Generative AI to enhance human decision-making and societal interactions, focusing on addressing biases, best practices in health and societal applications, and aligning AI with developer and user goals through measurement, feedback-driven reinforcement learning, and deployment strategies.

WHO SHOULD ENROLL

  • Engineers working as Product Managers, Web Application Developers/Managers, Software Service (SaaS) professionals, UX/UI Designers, Solutions Architects

  • Technical professionals looking to understand the potential of generative AI in their careers in various fields

  • No prior background in analytics, computer science, coding, or machine learning is required. 

Who Should Enroll Pic

THE MIT XPRO LEARNING EXPERIENCE

  • Learning technique

    LEARN BY DOING

    Practice processes and methods through simulations, assessments, case studies, and tools.

  • Learning technique

    LEARN FROM OTHERS

    Connect with an international community of professionals while working on projects based on real-world examples.

  • Learning technique

    LEARN ON DEMAND

    Access all of the content online and watch videos on the go.

  • Learning technique

    REFLECT AND APPLY

    Bring your new skills to your organization, through examples from technical work environments and ample prompts for reflection.

  • Learning technique

    DEMONSTRATE YOUR SUCCESS

    Earn a Professional Certificate and 3 Continuing Education Units (CEUs) from MIT.

  • Learning technique

    LEARN FROM THE BEST

    Gain insights from twelve esteemed MIT faculty and instructors from MIT's Computer Science and Artificial Intelligence Lab (CSAIL).

MIT FACULTY & INSTRUCTORS

Antonio Torralba

Antonio Torralba

Delta Electronics Professor of Electrical Engineering and Computer Science and Head of the AI+D faculty in the Electrical Engineering and Computer department, MIT

Daniela Rus

Daniela Rus

Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science; Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Deputy Dean of Research for Schwarzman College of Computing, MIT

Yoon Kim

Yoon Kim

Assistant Professor, Electrical Engineering and Computer Science, MIT

Regina Barzilay

Regina Barzilay

Professor, Department of Electrical Engineering and Computer Science; Faculty Co-Lead, MIT Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic), MIT

Armando Solar-Lezama

Armando Solar-Lezama

Professor, Associate Director and COO of CSAIL, MIT

Cynthia Breazeal

Cynthia Breazeal

Professor Media Arts and Sciences, Media Lab; and Dean for Digital Learning, Open Learning, MIT

Zach Lieberman

Zach Lieberman

Adjunct Associate Professor of Media Arts and Sciences, MIT

Pattie Maes

Pattie Maes

Professor of Media Arts and Sciences, MIT

Asu Ozdaglar

Asu Ozdaglar

MathWorks Professor of Electrical Engineering and Computer Science Department Head, Electrical Engineering and Computer Science & Deputy Dean of Academics, Schwarzman College of Computing, MIT

Wojciech Matusik

Wojciech Matusik

Professor of Electrical Engineering and Computer Science at the Computer Science and Artificial Intelligence Laboratory at MIT

Dylan Hadfield-Menell

Dylan Hadfield-Menell

Bonnie and Marty (1964) Tenenbaum Career Development Assistant Professor of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

Marzyeh Ghassemi

Marzyeh Ghassemi

Assistant Professor in Electrical Engineering and Computer Science, MIT

Phillip Isola

Phillip Isola

Associate Professor in MIT's Department of Electrical Engineering and Computer Science

THE BEST COMPANIES CONNECT WITH THE BEST MINDS AT MIT

Deepen your team’s career knowledge and expand their abilities with MIT xPRO’s online courses for professionals. Develop customized learning for your team with bespoke courses and programs on your schedule. Set a standard of knowledge and skills, leading to effective communication among employees and consistency across the enterprise.

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