Data Science Decoded
Mike E
We discuss seminal mathematical papers (sometimes really old 😎 ) that have shaped and established the fields of machine learning and data science as we know them today. The goal of the podcast is to introduce you to the evolution of these fields from a mathematical and slightly philosophical perspective.
We will discuss the contribution of these papers, not just from pure a math aspect but also how they influenced the discourse in the field, which areas were opened up as a result, and so on.
Our podcast episodes are also available on our youtube:
https://youtu.be/wThcXx_vXjQ?si=vnMfs
We will discuss the contribution of these papers, not just from pure a math aspect but also how they influenced the discourse in the field, which areas were opened up as a result, and so on.
Our podcast episodes are also available on our youtube:
https://youtu.be/wThcXx_vXjQ?si=vnMfs
Episodes
Mentioned books
Jul 7, 2024 • 1h 17min
Data Science #1 - Fisher RA. "On the mathematical foundations of theoretical statistics"(1922)
Explore the groundbreaking work of Ronald A. Fisher and how it shaped modern statistics. Delve into key concepts like maximum likelihood estimation and its role in parameter estimation. Discover the philosophical clash between frequentist and Bayesian approaches. Learn about the importance of latent representations in machine learning and their connection to Fisher information. The discussion highlights the evolution of statistical methods and the vital link between data compression techniques and statistical estimation.
