EXECUTIVE EDUCATION
EXECUTIVE EDUCATION
Join MIT Professional Education to build a cohesive AI ecosystem
February 23, 2021
8 weeks, live virtual
2 sessions/week
Special pricing up to 20% discount is available if you enroll with your colleagues. Please send an email to group-enrollments@emeritus.org for more information.
This program focuses on a system engineering approach — the development and deployment of AI initiatives. While participants are not required to have a deep technical background, having some familiarity with AI concepts will be helpful. This program is ideal for executives, technical leaders, product directors, and team leaders responsible for the successful deployment of AI capabilities and technologies.
AI will continue to revolutionize industries, some of which include:
Live Weekly Sessions with MIT Instructor
Special Guest Speakers Offering the ‘Voice of AI Practitioners’ across Multiple Industries
Review of Seminal AI Papers Illustrating ‘AI in Practice’
Application Exercises
Peer Learning and Networking
This live virtual program has 12 live sessions (two sessions per week). Each session is 90 minutes long.
Understand the challenges and key building blocks necessary for developing AI system capabilities.
Differentiate the classes of data that result from sensor inputs and data sources. Recognize the vulnerability of an AI system to data bias.
Categorize the different classes of machine learning algorithms from unsupervised learning and supervised learning to reinforcement learning. You will gain experience developing a simple neural network as part of a class exercise.
Understand what makes GPUs and TPUs well-matched with executing machine learning algorithms. Assess various computing technologies in terms of computation throughputs versus power consumed.
Classify areas where the application of AI augments the capabilities of humans working together as teams with the machine (or chiefly computer).
Assess the robustness of the AI system and ascertain the trustworthiness throughout its life cycle.
Formulate a strategic development plan that serves as the guiding blueprint for the AI designers, developers, testers, and users/customers.
Leverage a set of guidelines that incorporate people, processes, and technologies in the successful deployment of AI products and/or services.
Identify a set of practical performance metrics for assessing the effectiveness and productivity of AI teams.
Communicate the benefits of AI capabilities through the lens of a system perspective. This session leverages material from a recent book titled: Make it Clear: Speak and Write to Persuade and Inform by Patrick Henry Winston, published by The MIT Press, 2020.
Incorporate Responsible AI from the start of the development cycle, adhering to a set of ethics principles that best match your organization's goals and objectives, plus cultural environment.
Summary of all the topics covered in previous lectures serving as a quick reference guide for each of the participants.
Understand the challenges and key building blocks necessary for developing AI system capabilities.
Formulate a strategic development plan that serves as the guiding blueprint for the AI designers, developers, testers, and users/customers.
Differentiate the classes of data that result from sensor inputs and data sources. Recognize the vulnerability of an AI system to data bias.
Leverage a set of guidelines that incorporate people, processes, and technologies in the successful deployment of AI products and/or services.
Categorize the different classes of machine learning algorithms from unsupervised learning and supervised learning to reinforcement learning. You will gain experience developing a simple neural network as part of a class exercise.
Identify a set of practical performance metrics for assessing the effectiveness and productivity of AI teams.
Understand what makes GPUs and TPUs well-matched with executing machine learning algorithms. Assess various computing technologies in terms of computation throughputs versus power consumed.
Communicate the benefits of AI capabilities through the lens of a system perspective. This session leverages material from a recent book titled: Make it Clear: Speak and Write to Persuade and Inform by Patrick Henry Winston, published by The MIT Press, 2020.
Classify areas where the application of AI augments the capabilities of humans working together as teams with the machine (or chiefly computer).
Incorporate Responsible AI from the start of the development cycle, adhering to a set of ethics principles that best match your organization's goals and objectives, plus cultural environment.
Assess the robustness of the AI system and ascertain the trustworthiness throughout its life cycle.
Summary of all the topics covered in previous lectures serving as a quick reference guide for each of the participants.
Note: Sessions are held on Tuesdays and Fridays, 8:30 - 10:30 am ET. No live session in the first week. For full session schedule, please download the brochure.
Download Brochure
David Martinez
Lead Instructor, Laboratory Fellow and Former Associate Division Head, Cyber Security and Information Sciences, MIT Lincoln Laboratory
David Martinez is a Laboratory Fellow in the Cyber Security and Information Sciences Division at MIT Lincoln Laboratory. In this capacity, he is focusing on research and technical directions in the areas of artificial intelligence (AI), high-performance computing, and digital-enterprise transformation. Previously, Mr. Martinez served as an Associate Head in the Cyber Security and Information Sciences Division. He was also a member of Lincoln Laboratory's Steering Committee.
Mr. Martinez has held many past Laboratory leadership roles, including Leader of the Embedded Digital Systems Group and Head of the ISR Systems and Technology Division. Mr. Martinez also served in a leadership role as President and Chairman of Mercury Federal Systems. In 2011, Mr. Martinez became Principal Staff in the Communication Systems and Cyber Security Division, where he focused on technical and programmatic development in the areas of cybersecurity, enterprise architectures, system applications, and cloud computing.
Prior to joining Lincoln Laboratory, he was employed as a principal research engineer at ARCO Oil and Gas Company, specializing in adaptive seismic signal processing. He received the ARCO special achievement award. He holds three U.S. patents based on his work in signal processing for seismic applications.
Early in Mr. Martinez' career at the Laboratory, he led major accomplishments through the development of a series of unique high-performance embedded computing processors for a classied Laboratory program. The processor work for this program was successfully transitioned to a major defense acquisition program, and the influence of the technology work that he co-developed continues to have an impact in the Department of Defense. He was elected an IEEE Fellow "for technical leadership in the development of high-performance embedded computing for real-time defense systems." In 2008, he and his co-authors released a successful book titled High Performance Embedded Computing Handbook, which is highly referenced within the embedded computing research community. Mr. Martinez also had a major role in establishing the Laboratory's High Performance Embedded Computing (HPEC) workshop, which transitioned in 2012 to become a major annual IEEE conference.
Mr. Martinez holds a BS degree from NMSU, an SM degree from MIT, and an EE degree jointly from MIT and the Woods Hole Oceanographic Institution in electrical engineering and oceanographic engineering. He completed an MBA at the Southern Methodist University. From 1999 to 2004, Mr. Martinez served as a member of the Army Science Board. From 2007 to 2008, he served on the Defense Science Board ISR Task Force.
Bruke Kifle
Get recognized! Upon successful completion of the program, MIT Professional Education provides a certificate of completion to participants. This program is scored as a pass or no-pass; participants must attend 10 out of 12 Live Sessions and complete assigned knowledge tests and class project to pass and obtain the certificate of completion.
This program may be taken as a stand-alone course or as part of the larger MIT Professional Education Certificate Program in Machine Learning and Artificial Intelligence. This professional certificate equips you with the best practices and actionable knowledge needed to put you and your organization at the forefront of the AI revolution.
Download BrochureNote: Attendance will be tracked on Zoom. Participants should notify program support by email as early as possible in case of absence(s).
Participants are expected to watch the video recordings of any missed sessions.
Flexible payment options available.