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
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Signup to Generate a PitchWe 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.
Data Science Decoded is hosted by Mike E. The show has published 33 episodes to date.
Recent episodes include "Data Science #34 - The deep learning original paper review, Hinton, Rumelhard & Williams (1985)" (23 November 2025, 47 minutes), "Data Science #33 - The Backpropagation method, Paul Werbos (1980)" (3 November 2025, 58 minutes), and "Data Science #32 - A Markovian Decision Process, Richard Bellman (1957)" (19 September 2025, 46 minutes).
Data Science Decoded publishes roughly every few months, with episodes averaging 44 minutes across 33 episodes.
Booking probability for Data Science Decoded is 31%, and the host is rated Selective on guests. Data Science Decoded requires a Pod Score of 81 or higher. Data Science Decoded's contact route on record: verified email.
The strongest fits for Data Science Decoded are Scientific Research & Development. Data Science Decoded has a science audience.
Mike E hosts Data Science Decoded, a science podcast with 33 episodes published.
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Episode #34
<p>On the 34th episode, we review the 1986 paper, "Learning representations by back-propagating errors" , which was pivotal because it provided a clear, generalized framework for training neural networks with internal...
<p>On the 33rd episdoe we review Paul Werbos’s “Applications of Advances in Nonlinear Sensitivity Analysis” which presents efficient methods for computing derivatives in nonlinear systems, drastically reducing...
<p>We reviewed Richard Bellman’s “A Markovian Decision Process” (1957), which introduced a mathematical framework for sequential decision-making under uncertainty. </p><p><br></p><p>By connecting recurrence relations to...
<p>On the 31st episode of the podcast, we add Liron to the team, we review a gem from 1921, where Sewall Wright introduced path analysis, mapping hypothesized causal arrows into simple diagrams and proving that any...
Data Science Decoded
<p>In the 30th episode we review the the bootstrap, method which was introduced by Bradley Efron in 1979, is a non-parametric resampling technique that approximates a statistic’s sampling distribution by repeatedly...
<p>In the 29th episode, we go over the 1979 paper by Gordon Vivian Kass that introduced the CHAID algorithm.CHAID (Chi-squared Automatic Interaction Detection) is a tree-based partitioning method introduced by G. V...
<p>In the 28th episode, we go over Burton Bloom's Bloom filter from 1970, a groundbreaking data structure that enables fast, space-efficient set membership checks by allowing a small, controllable rate of false...
<p>Mansfield Merriman's 1877 paper traces the historical development of the Method of Least Squares, crediting Legendre (1805) for introducing the method, Adrain (1808) for the first formal probabilistic proof, and...
<p>In this episode, we review Cauchy’s 1847 paper, which introduced an iterative method for solving simultaneous equations by minimizing a function using its partial derivatives. Instead of elimination, he proposed...
<p>At the 24th episode we go over the paper titled:Dempster, Arthur P., Nan M. Laird, and Donald B. Rubin. "Maximum likelihood from incomplete data via the EM algorithm." Journal of the royal statistical society: series...
<p>In the 23rd episode we review the The 1953 paper Metropolis, Nicholas, et al. "Equation of state calculations by fast computing machines." </p> <p>The journal of chemical physics 21.6 (1953): 1087-1092 which...
We review Richard Bellman's "The Theory of Dynamic Programming" paper from 1954 which revolutionized how we approach complex decision-making problems through two key innovations. First, his Principle of Optimality...
In the 1st episode of the second season we review the legendary Marvin Minsky's "Steps Toward Artificial Intelligence" from 1961. Itis a foundational work in the field of AI that outlines the challenges and...
In the 20th episode, we review the seminal paper by Rao which introduced the Cramer Rao bound: Rao, Calyampudi Radakrishna (1945). "Information and the accuracy attainable in the estimation of statistical parameters"...
<p>In this episode with go over the Kullback-Leibler (KL) divergence paper, "On Information and Sufficiency" (1951). It introduced a measure of the difference between two probability distributions, quantifying the cost...
<p>In the 18th episode we go over the original k-nearest neighbors algorithm; Fix, Evelyn; Hodges, Joseph L. (1951). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties USAF School of Aviation...
We review the original Monte Carlo paper from 1949 by Metropolis, Nicholas, and Stanislaw Ulam. "The monte carlo method." Journal of the American statistical association 44.247 (1949): 335-341. The Monte Carlo method...
<p>In the 16th episode we go over the seminal the 1952 paper titled: "A stochastic approximation method." The annals of mathematical statistics (1951): 400-407, by Robbins, Herbert and Sutton Monro. The paper introduced...
<p>the 15th episode we went over the paper "Problems in the Analysis of Survey Data, and a Proposal" by James N. Morgan and John A. Sonquist from 1963. It highlights seven key issues in analyzing complex survey data...
<p>In the 15th episode we went over the paper "Problems in the Analysis of Survey Data, and a Proposal" by James N. Morgan and John A. Sonquist from 1964. It highlights seven key issues in analyzing complex survey data...
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