The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events Specialist
Uncover limitless possibilities with AI
June 28, 2022
8 weeks, online
6 hours per week
US$2,950 US$2,714 or get US$295 off with a referral
Our participants tell us that taking this program together with their colleagues helps to share common language and accelerate impact.
We hope you find the same. Special pricing is available for groups.
The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events SpecialistBased on the information you provided, your team is eligible for a special discount, for Designing and Building AI Products and Services starting on June 28, 2022 .
We’ve sent you an email with enrollment next steps. If you’re ready to enroll now, click the button below.
Have questions? Email us at group-enrollments@emeritus.org.Deploying the right AI technologies in your organization can help you automate routine tasks, gain insights through data analytics, and engage better with customers. Given the broad spectrum of AI’s applications in organizations, it’s no surprise that AI Specialist is the top-rated job on the LinkedIn Emerging Jobs Report in 2020. With an annual growth rate of 74% for this position, every industry is clamoring for AI talent to first devise a strategic plan for AI applications, and then help manage and optimize them in practice.
Global CEOs state digital transformation technologies including AI will be their top area for long-term investments
Technology jobs — mainly in the area of digital transformation – to be added globally over five years
This 8-week course is ideal for you if you are a technical product leader, technology professional, technology consultant, or entrepreneur who wants to enhance your understanding of AI technology fundamentals and tools, and explore various design processes involved in AI-based products. Knowledge of calculus, linear algebra, statistics, and probabilities is beneficial, along with basic Python experience. The program is ideal for:
Build a foundation
Expand your knowledge
Apply learnings
Become proficient
Earn a certificate and 5 Continuing Education Units (CEUs) from MIT xPRO
Insights and examples from renowned MIT faculty
Market-ready skills for evaluating the opportunity for AI solutions and making the case for it
Develop an AI project proposal to present to internal stakeholders or investors
Advance your knowledge through crowdsourcing, demos, and design-support activities
Get acquainted with the stages involved in designing an AI-based product with a focus on specifics such as the cost metrics and technical requirements of an AI software development plan.
Identify various machine learning algorithms and study the different approaches such as Bayesian and regression models. Learn about unsupervised and semi-supervised methods of machine learning algorithms. Run and analyze the results from various machine learning algorithms.
Building on the knowledge of machine learning fundamentals gained in Week 2, explore the basics of deep learning. The topics include neural networks, artificial neurons, and simulation of complex networks.
Identify superhuman intelligence used in an AI product. Compare and contrast the advantages and disadvantages of using an AI technology.
Use the resources provided in this module to understand the techniques, application areas, benefits, and drawbacks of HCI. Learn to define an appropriate level of machine involvement in interactions with humans and computers. Seek ways to use artificial intelligence to your advantage.
Get an introduction to the concept of superminds, and compare and contrast the different types of superminds. Analyze how humans and machines can work together to surpass the sum of their parts. Apply cognitive processes to various organizations and community problems.
Learn how artificial intelligence and Generative Adversarial Networks (GANs) can be used to generate fake images and videos from real data. Assess the technical, social, and economic impact of AI technologies.
Implement the Lawler Model to define an AI problem. Design and construct a summary of an AI product or process using learnings from the previous modules of the program.
Get acquainted with the stages involved in designing an AI-based product with a focus on specifics such as the cost metrics and technical requirements of an AI software development plan.
Use the resources provided in this module to understand the techniques, application areas, benefits, and drawbacks of HCI. Learn to define an appropriate level of machine involvement in interactions with humans and computers. Seek ways to use artificial intelligence to your advantage.
Identify various machine learning algorithms and study the different approaches such as Bayesian and regression models. Learn about unsupervised and semi-supervised methods of machine learning algorithms. Run and analyze the results from various machine learning algorithms.
Get an introduction to the concept of superminds, and compare and contrast the different types of superminds. Analyze how humans and machines can work together to surpass the sum of their parts. Apply cognitive processes to various organizations and community problems.
Building on the knowledge of machine learning fundamentals gained in Week 2, explore the basics of deep learning. The topics include neural networks, artificial neurons, and simulation of complex networks.
Learn how artificial intelligence and Generative Adversarial Networks (GANs) can be used to generate fake images and videos from real data. Assess the technical, social, and economic impact of AI technologies.
Identify superhuman intelligence used in an AI product. Compare and contrast the advantages and disadvantages of using an AI technology.
Implement the Lawler Model to define an AI problem. Design and construct a summary of an AI product or process using learnings from the previous modules of the program.
BRIAN SUBIRANA
ANDREW LIPPMAN
STEFANIE MUELLER
DUANE BONING
BRUCE LAWLER
THOMAS W. MALONE
BARBARA H. WIXOM
BRIAN SUBIRANA
Director, MIT Auto-ID lab; Director, MIT and Accenture Convergence Initiative for Industry and Technology
Brian Subirana has taught at MIT Sloan and the MIT School of Engineering and he is also on the faculty of Harvard University. His research centers on IoT and AI, and focuses on manufacturing, e-learning, the creative industries, and digital health. He is developing a voice name system that can help humans talk to any object in an IoT environment. He has over 200 publications, including three books, and is currently researching open standards for AI and IoT.
Subirana earned his doctorate in computer vision at the MIT Artificial Intelligence Laboratory (now CSAIL) and his MBA at MIT Sloan.
ANDREW LIPPMAN
Senior Research Scientist, MIT; Associate Director, MIT Media Lab
Andrew Lippman heads the Viral Communications research group at MIT Media Lab. His work has ranged from digital video and entertainment to graphical interfaces, networking and blockchains. In the 1980s, Lippman developed the Movie Map that presaged Google’s Street View. He helped pioneer visual imaging and communications systems such as MPEG and digital HDTV. He has written both technical and mainstream articles about our digital future, and given over 250 presentations throughout the world on the future of information and its commercial and social impacts.
Lippman received his bachelor’s and master’s degrees in science at MIT and doctorate at École Polytechnique Fédérale (EPFL) in Lausanne, Switzerland.
STEFANIE MUELLER
X-Career Development Assistant Professor, MIT Electrical Engineering and Computer Science, joint with Mechanical Engineering
Stefanie Mueller is the head of the Human Computer Interaction Communities of Research (HCI CoR) at MIT CSAIL. In her research, she develops novel hardware and software systems that advance personal fabrication technologies. Mueller has received an NSF CAREER Award, an Alfred P. Sloan Fellowship, a Microsoft Research Faculty Fellowship, and was also named one of Forbes 30 under 30 in Science. She publishes her work at the most selective human-computer interaction forums of CHI and UIST and has received a best paper award and two best paper nominations in the past.
Mueller earned her Ph.D. in computer science at Hasso Plattner Institute in Germany.
DUANE BONING
Clarence J. Lebel Professor, Electrical Engineering and Computer Science
Duane Boning is affiliated with the MIT Microsystems Technology Laboratories and serves as Associate Director for Computation and CAD (computer-aided design). He is also the Engineering Faculty Co-Director of the MIT Leaders for Global Operations program. His research focus is machine learning and statistical methods for modeling, and control of variation in manufacturing. His work is centered on statistical characterization and design for manufacturing of devices and circuits in advanced technologies, and the modeling of chemical mechanical polishing, spin-on coatings, plasma etch, and nano-imprint/embossing processes. His work has appeared in over 280 journals and conference publications.
Boning earned his bachelor’s and master’s in science, and doctorate in electrical engineering and computer science at MIT.
BRUCE LAWLER
Managing Director, MIT Machine Intelligence for Manufacturing and Operations (MIMO)
Bruce Lawler is a technology entrepreneur and an executive leader. He has developed several applications across platforms such as mobile, SaaS, AI and video distribution networks. He has headed multiple ventures in fields ranging from consumer and industrial hardware to wireless and video network operations. As the managing director MIT MIMO, Lawler focuses on resolving the data and operational challenges in manufacturing with measurable and impactful efficiency, and revenue improvement.
Lawler earned his bachelor’s in engineering at Purdue University and his master’s in engineering and MBA from MIT’s Sloan School.
THOMAS W. MALONE
Patrick J. McGovern Professor of Management, MIT Sloan Founding Director, MIT Center for Collective Intelligence
Thomas W. Malone is the Professor of Information Technology and a Professor of Work and Organizational Studies at MIT. In his researches over the years, Malone rightly predicted major business and technology trends decades before they happened. For instance, he first wrote about video games and the concept of “gamification” as early as 1980, and in an article in 1987 he predicted many of the major developments in e-commerce, which we have seen in the last 25 years. Malone has published over 100 articles, research papers and book chapters, and has co-written four books.
Malone earned his Ph.D. at Stanford University and an honorary doctorate from the University of Zurich.
BARBARA H. WIXOM
Principal Research Scientist, MIT Center for Information Systems Research (CISR)
Wixom leads the MIT CISR Data Research Advisory Board, comprised of data and analytics executives from CISR organizations. Her research explores how organizations generate business value from data assets. She has deep expertise in data and analytics techniques and technologies, with particular interest in data and analytics strategy, capabilities, and success. Prior to joining MIT CISR, Wixom enjoyed a 15-year academic career at the University of Virginia.
DAVID ANDERTON-YANG
ARUNA SANKARANARAYANAN
Get recognized! Upon successful completion of this program, MIT xPRO grants a certificate of completion to participants and 5 Continuing Education Units (CEUs). This program is graded as a pass or fail; participants must receive 75% to pass and obtain the certificate of completion.
Download BrochureNote: After successful completion of program, your verified digital certificate will be emailed, at no additional cost, in the name you used when registering for the program. All certificate images are for illustrative purposes only and may be subject to change at the discretion of MIT.
Flexible payment options available.