AI for Managers
9:00am - 5:00pm
What you'll learn, and how you can apply it
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Introduction to AI and Data Science
Terms and definitions: what does machine learning mean?
Historical context and present day
Drivers for AI and data science
What’s so different about big data?
AI is eating the world
Making AI practical
Algorithms and Techniques
Data formats, databases, and schemas
Evaluating model performance and validating models
Terminology: regression, classification, supervised and unsupervised
Advanced models: random forests, support vector machines, deep learning, neural networks
Industry Use Cases
AI Within the Organization
Maturity levels for AI
Evaluating good projects for AI
Build vs. Buy and Hire vs. Train
Skills, tools, platforms needed for AI
Structuring Data and AI Initiatives within your organization: successful and cautionary tales
Common Pitfalls and Fallacies in AI and Data Science
AI and data science in the headlines: the good, the bad, and the ugly
Legal and regulatory implications
Litigation and liabilities of bad data science
Common fallacies in data science and AI
Lying with statistics and how to spot it
About your instructors
Dylan Bargteil is a data scientist in residence at the Data Incubator, where he works on research-guided curriculum development and instruction. Previously, he worked with deep learning models to assist surgical robots and was a research and teaching assistant at the University of Maryland, where he developed a new introductory physics curriculum and pedagogy in partnership with HHMI. Dylan studied physics and math at University of Maryland and holds a PhD in physics from New York University.
Tianhui Michael Li is the founder and CEO of the Data Incubator. Michael has worked as a data scientist lead at Foursquare, a quant at D.E. Shaw and JPMorgan, and a rocket scientist at NASA. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves. He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.
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