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What is Statistics? (Michael I. Jordan) | AI Podcast Clips
7 segments available
Full episode with Michael I. Jordan (Feb 2020): https://www.youtube.com/watch?v=EYIKy_FM9x0 Clips channel (Lex Clips): https://www.youtube.com/lexclips Main channel (Lex Fridman): https://www.youtube.com/lexfridman (more links below) Podcast full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Podcasts clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 Podcast website: https://lexfridman.com/ai Podcast on Apple Podcasts (iTunes): https://apple.co/2lwqZIr Podcast on Spotify: https://spoti.fi/2nEwCF8 Podcast RSS: https://lexfridman.com/category/ai/feed/ Michael I Jordan is a professor at Berkeley, and one of the most influential people in the history of machine learning, statistics, and artificial intelligence. He has been cited over 170,000 times and has mentored many of the world-class researchers defining the field of AI today, including Andrew Ng, Zoubin Ghahramani, Ben Taskar, and Yoshua Bengio. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
Segments Timeline
Defining Statistics
Michael I. Jordan introduces the concept of statistics, explaining its role as a bridge between mathematics, science, and technology. He emphasizes the importance of making inferences and decisions based on statistical principles, highlighting the necessity of understanding errors and probabilities in the decision-making process.
"an absurd question but what is statistics so the here it's a little bit it's somewhere between math and science and technology it's somewhere in that convex hull so it's some principles that allow you..."
The Historical Roots of Statistics
Jordan delves into the history of statistics, tracing its origins back 250 years to the concept of inverse probability. He discusses how early statistics were developed to explain gambling outcomes and how Laplace formalized the field to analyze data for state governance, coining the term 'statistics' from the French word for state.
"parts it may be the big part yeah so the original so statistics you know short history was that you know it's Carter goes back this sort as a formal discipline you know 250 years or so it was called i..."
Statistics and Decision Theory
In this segment, Jordan explains the evolution of statistics alongside decision theory and game theory. He discusses how these fields intersect and the importance of decision-making in statistics, highlighting the foundational role of decision theory in modern statistical curricula.
"all statistics ever since but but by the time it got formalized it was sort of in the 30s and around that time there was game theory and decision theory developed and nearby people that era didn't thi..."
Bayesian vs. Frequentist Approaches
Jordan contrasts Bayesian and frequentist approaches to statistics, explaining their differing philosophies regarding uncertainty and decision-making. He discusses how each perspective influences statistical analysis and the importance of understanding both frameworks in practice.
"statistics what is the most beautiful mysterious may be surprising idea that you've come across yeah good question um I mean there's a bunch of surprising ones there's something it's way too technical..."
Understanding Loss Functions
In this segment, Jordan elaborates on loss functions in decision theory, explaining how they relate to Bayesian and frequentist methods. He discusses the implications of these approaches for real-world applications, particularly in software development and scientific research.
"to in the field can you define Beijing and frequentist yeah decision theory you can make I have a like I have a video that people could see it's called are you amazing or a frequentist and kind of hel..."
Empirical Bayes and Human Expertise
Jordan introduces the concept of empirical Bayes, which combines Bayesian frameworks with real-world uncertainties. He discusses how this approach allows for the integration of human expertise into statistical models, enhancing their applicability and reliability.
"out there I'm trying to infer the average height of the population well I have an idea of roughly what the height is so I'm gonna over the the the theta so now that loss function as only now again one..."
False Discovery Rate Explained
In this final segment, Jordan explains the false discovery rate, a critical concept in hypothesis testing. He contrasts it with traditional accuracy metrics, emphasizing its Bayesian interpretation and the importance of understanding the probability of false discoveries in statistical analysis.
"assumptions this thing will work so Pratt you asked question was my favorite you know or was the most surprising nice idea so one that is more accessible as something called false discovery rate which..."