Description

Statistics and hypothesis testing are the foundation of all our data-driven innovations including machine learning and generative AI. But with all this availability of data and modeling, it is easy to lose sight of the scientific method and its role. In this session, we will learn the fundamentals of descriptive and inferential statistics, and how they relate to machine learning and data mining. This will include understanding the relationship between a sample and a population, the p-value, and how we measure truth. We will also talk about the dangers of p-hacking and how it arises in data-driven environments.

Instructor's Bio

Thomas Nield

Founder at Nield Consulting Group and Yawman Flight

Thomas Nield is the founder of Nield Consulting Group and Yawman Flight, as well as an instructor at University of Southern California. He enjoys making technical content relatable and relevant to those unfamiliar or intimidated by it. Thomas regularly teaches classes on data analysis, machine learning, mathematical optimization, and practical artificial intelligence. At USC he teaches AI System Safety, developing systematic approaches for identifying AI-related hazards in aviation and ground vehicles. He's authored three books, including Essential Math for Data Science (O’Reilly) and Getting Started with SQL (O'Reilly) 
 He is also the founder and inventor of Yawman Flight, a company developing universal handheld flight controls for flight simulation and unmanned aerial vehicles.

Webinar

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