EXECUTIVE EDUCATION

AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment

Join MIT Professional Education to build a cohesive AI ecosystem

Get Your Brochure

Course Dates

STARTS ON

February 23, 2021

Course Duration

DURATION

8 weeks, live virtual
2 sessions/week

Course Duration

PROGRAM FEE

US$3,500

Course Information Flexible payment available

Who Should Attend

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:

  • Energy
  • Consumer products and services
  • Mobility/transportation/driverless vehicles
  • Financial Services
  • Manufacturing/industrial products
  • Healthcare
  • National security

Your Learning Journey

Your Live Virtual Experience

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

Program Topics

This live virtual program has 12 live sessions (two sessions per week). Each session is 90 minutes long.

Session 1:

AI Background and System Architecture Overview

Understand the challenges and key building blocks necessary for developing AI system capabilities.

Session 2:

Data Conditioning

Differentiate the classes of data that result from sensor inputs and data sources. Recognize the vulnerability of an AI system to data bias.

Session 3:

Machine Learning

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.

Session 4:

Modern Computing

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.

Session 5:

Human-Machine Teaming

Classify areas where the application of AI augments the capabilities of humans working together as teams with the machine (or chiefly computer).

Session 6:

Robust AI

Assess the robustness of the AI system and ascertain the trustworthiness throughout its life cycle.

Session 7:

AI Strategic Vision and Project Roadmap

Formulate a strategic development plan that serves as the guiding blueprint for the AI designers, developers, testers, and users/customers.

Session 8:

Guidelines for Deploying AI Capabilities

Leverage a set of guidelines that incorporate people, processes, and technologies in the successful deployment of AI products and/or services.

Session 9:

Fostering an Innovative Team Environment

Identify a set of practical performance metrics for assessing the effectiveness and productivity of AI teams.

Session 10:

Communicating Effectively

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.

Session 11:

Responsible AI

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.

Session 12:

Putting It All Together

Summary of all the topics covered in previous lectures serving as a quick reference guide for each of the participants.

Session 1:

AI Background and System Architecture Overview

Understand the challenges and key building blocks necessary for developing AI system capabilities.

Session 7:

AI Strategic Vision and Project Roadmap

Formulate a strategic development plan that serves as the guiding blueprint for the AI designers, developers, testers, and users/customers.

Session 2:

Data Conditioning

Differentiate the classes of data that result from sensor inputs and data sources. Recognize the vulnerability of an AI system to data bias.

Session 8:

Guidelines for Deploying AI Capabilities

Leverage a set of guidelines that incorporate people, processes, and technologies in the successful deployment of AI products and/or services.

Session 3:

Machine Learning

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.

Session 9:

Fostering an Innovative Team Environment

Identify a set of practical performance metrics for assessing the effectiveness and productivity of AI teams.

Session 4:

Modern Computing

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.

Session 10:

Communicating Effectively

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.

Session 5:

Human-Machine Teaming

Classify areas where the application of AI augments the capabilities of humans working together as teams with the machine (or chiefly computer).

Session 11:

Responsible AI

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.

Session 6:

Robust AI

Assess the robustness of the AI system and ascertain the trustworthiness throughout its life cycle.

Session 12:

Putting It All Together

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

Program Instructor

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... More info

Course Assistant

Bruke Kifle

Course Assistant

Bruke Kifle is a computer scientist and product leader passionate about the intersection of Artificial Intelligence, business, ethics and society. His experiences span academia and industry in research, software engineering and product management. He is a socio-technologist motivated by the ability to improve human conditions in low-resource environments through technical solutions. An innovator and educator, Bruke has founded and scaled new initiatives, teaching programs, and research projects both domestically and internationally in countries including Ethiopia, France, Morocco and South Africa.

Bruke is currently an AI Product Manager at Microsoft NERD. He received his Bachelor's in Computer Science and Management, and Master's in Electrical Engineering and Computer Science from MIT, where his academic and research interests were broadly in the fields of responsible Artificial Intelligence, algorithms and their applications for social good.

Certificate of Completion

Certificate of Completion

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 Brochure


Note: 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.

Apply Now

Early registrations are encouraged. Seats fill up quickly!

Flexible payment options available. Learn more.