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 1min
Data Science #2 - "Application of the Logistic Function to Bio-Assays" (1944), Berkson Joseph
"Application of the Logistic Function to Bio-Assays" (1944), Berkson Joseph
It gained further prominence in the 20th century through applications in various fields, including biology and bio-assay. Joseph Berkson's 1944 paper, 'Application of the Logistic Function to Bio-Assay,' was pivotal in popularizing its use for estimating drug potency.
Berkson argued that the logistic function was a more statistically manageable and theoretically sound alternative to the probit function, which assumed that individual susceptibilities to a drug follow a normal distribution.
The logistic function's ability to be easily linearized via the logit transformation simplifies parameter estimation, making it an attractive choice for analyzing dose-response data.
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.
