Strategic Insight from Machine Learning
This course emphasizes defining viable machine learning problems and assessing machine learning models. The course straddles two extremes: exposing students to cutting-edge tools and processes, while also asking students to understand those techniques implications in actual ML/AI industry deployments. This combination of grounded application and high-level reflection trains students to anticipate how ML/AI can (and can't) be used for business processes and decision-making, thus training students to make business decisions about where to apply machine learning and AI methods. The class is highly discursive, and students are generally very engaged. Though the underlying content is technical, the course remains at a high level of abstraction, and thus does not have any statistics, programming, or math prerequisites. The course seeks to train students to answer the qualitative questions what projects are viable for ML/AI investment, and how will I assess performance?
Strategic Insight from Machine Learning
This course emphasizes defining viable machine learning problems and assessing machine learning models. The course straddles two extremes: exposing students to cutting-edge tools and processes, while also asking students to understand those techniques implications in actual ML/AI industry deployments. This combination of grounded application and high-level reflection trains students to anticipate how ML/AI can (and can't) be used for business processes and decision-making, thus training students to make business decisions about where to apply machine learning and AI methods. The class is highly discursive, and students are generally very engaged. Though the underlying content is technical, the course remains at a high level of abstraction, and thus does not have any statistics, programming, or math prerequisites. The course seeks to train students to answer the qualitative questions what projects are viable for ML/AI investment, and how will I assess performance?
This course emphasizes defining viable machine learning problems and assessing machine learning models. The course straddles two extremes: exposing students to cutting-edge tools and processes, while also asking students to understand those techniques implications in actual ML/AI industry deployments. This combination of grounded application and high-level reflection trains students to anticipate how ML/AI can (and can't) be used for business processes and decision-making, thus training students to make business decisions about where to apply machine learning and AI methods. The class is highly discursive, and students are generally very engaged. Though the underlying content is technical, the course remains at a high level of abstraction, and thus does not have any statistics, programming, or math prerequisites. The course seeks to train students to answer the qualitative questions what projects are viable for ML/AI investment, and how will I assess performance?
John Chandler Johnson
Associate Professor
Department of Marketing
Personal Profile：John Chandler Johnson is an Associate Professor in BI Norwegian Business School's Department of Strategy and Entrepreneurship. Chandler's current research formalizes micro sociological theories of endogenous social network evolution, which he uses to computationally study knowledge propagation and market disequilibrium dynamics. Chandler teaches machine learning at the executive, EMBA, and MSc levels. Chandler holds a Sociology PhD from Stanford, a Statistics MS from Stanford, a Social Science MA from the University of Chicago, and an Economics BA from the University of Washington. Chandler's current teaching is in applied machine learning. He has industry experience in operations consulting, algorithm development, and machine learning model deployment. Chandler began his career managing Beijing operations for a Shanghai market research startup.