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—"Creating an AI course for business leaders so they can speak the same language as their engineers is challenging, but working with a variety of companies to understand the practical, technical, and commercial hurdles was incredibly insightful. Now, these learnings are available to all leaders who want to understand more about how to leverage Artificial Intelligence to advance their business."
Erik Brynjolfsson, Professor of Management at MIT Sloan & Director of MIT’s Initiative on Digital Economy
To optimize your chances of success in this Executive Program, we recommend prior exposure to statistics and probability, as well as experience in business decision-making in an IT or technical environment.See detailed requirements.
Understand how to apply probabilistic reasoning to machine learning, and gain a working knowledge of the key terms and components involved in machine learning approaches, such as: algorithm, model, training, feature, test set, training set, and ground truth dataset. Then, develop ideas for machine learning and AI use cases for a business, and evaluate them for feasibility and impact.
Understand how critical data attributes can affect a machine learning model, and distinguish the differences between classification, regression, optimization, and simulation in ML/AI applications. Become familiar with the applications of deep learning and how it can be applied to predictive modeling, reinforcement learning models, and optimization.
Understand the importance, applications, and components of machine learning model architecture including classifiers, regressors, optimizers, simulators, policy learners, and segmenters. Differentiate between the capabilities of natural language processing, voice/speech processing, and computer vision. Finally, build machine learning model architectures for a digital channel chatbot, negotiation engine, and visual classifier.
Learn how to label data for supervised learning. Understand the fundamental requirements of AI infrastructure, and how to overcome common implementation hurdles. Assess the feasibility of AI use cases in a range of business scenarios by evaluating data readiness.
Define the parameters for designing machine learning models including accuracy, underfitting and overfitting of data, and ethical frameworks.
Learn how to build surveys and conduct interviews to solicit feedback on model prototypes. Identify key stakeholders inside and outside an organization to provide feedback in an iterative design process. Analyze the results of feedback from stakeholders to inform evaluation and prioritization of business use cases.
Learn how to begin implementing AI use cases with small learning experiments, and build a roadmap deploying machine learning applications that strategically complement one another. Finally, create a proposal that integrates key use cases into a transformational business story.
Draw on all of the skills learned throughout the lessons to create an ML/AI strategy that is technically achievable and highly impactful on your business based on the evaluation of various AI-enabled use cases.Project Details
Founder, Product Manager, & Corporate Development Leader
William Ross is an experienced investor in AI and ML, and previously worked with IBM's Watson group managing a variety of PM and corporate dev teams. Today, he is the co-founder of a Silicon Valley-based AI startup. He attended Stanford's Graduate School of Business.
AI Engineer at Apple
Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.
Data Scientist at Nerd Wallet
Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.
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