AI for Managers
9:00am - 5:00pm
What you'll learn, and how you can apply it
This training is for you because...
Hardware and/or installation requirements:
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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