Data Science Decoded

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

Data Science Decoded is a science podcast hosted by Mike E, with 33 episodes on record and a Required Pod Score of 81. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Mike E hosts Data Science Decoded, a science show with 33 episodes published.

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  Episode #34

  Data Science #34 - The deep learning original paper review, Hinton, Rumelhard & Williams (1985)

  Nov 23, 202547 minS2

  <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 'hidden' units. The core of the procedure, back-propagation, repeatedly adjusts the weights of connections in the network to minimize the error between the actual and desired output vectors. Crucially, this process forces the hidden units, whose desired states aren't specified, to develop distributed internal representations of the task domain's important features.This capability to construct useful new features distinguishes back-propagation from earlier, simpler methods like the perceptron-convergence procedure. The authors...

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  Episode #33

  Data Science #33 - The Backpropagation method, Paul Werbos (1980)

  Nov 3, 202558 minS2

  <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 computational costs for large-scale models. </p><p><br></p><p>Werbos, Paul J. "Applications of advances in nonlinear sensitivity analysis." System Modeling and Optimization: Proceedings of the 10th IFIP Conference New York City, USA, August 31–September 4, 1981</p><p><br></p><p>These methods, especially the backward differentiation technique, enable better sensitivity analysis, optimization, and stochastic modeling across economics, engineering, and artificial intelligence. </p><p><br></p><p>The paper also introduces Generali...

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  Episode #32

  Data Science #32 - A Markovian Decision Process, Richard Bellman (1957)

  Sep 19, 202546 minS2

  <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 Markov processes, Bellman showed how current choices shape future outcomes and formalized the principle of optimality, laying the groundwork for dynamic programming and the Bellman equationThis paper is directly relevant to reinforcement learning and modern AI: it defines the structure of Markov Decision Processes (MDPs), which underpin algorithms like value iteration, policy iteration, and Q-learning. </p><p><br></p><p>From robotics to large-scale systems like AlphaGo, nearly all of RL tra...

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  Episode #4

  Data Science #31 - Correlation and causation (1921), Wright Sewall

  Jul 26, 202548 minS2

  <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 sample correlation can be written as the sum of products of “path coefficients.” </p><p><br /></p><p>By treating each arrow as a standardised regression weight, he showed how to split the variance of an outcome into direct, indirect, and joint pieces, then solve for unknown paths from an ordinary correlation matrix—turning the slogan “correlation ≠ causation” into a workable calculus for observational data.Wright’s...

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  Data Science Decoded

  Data Science #30 - The Bootstrap Method (1977)

  May 30, 202541 minS0

  <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 drawing with replacement from the observed data, allowing estimation of standard errors, confidence intervals, and bias without relying on strong distributional assumptions. </p><p><br></p><p>Its ability to quantify uncertainty cheaply and flexibly underlies many staples of modern data science and AI, powering model evaluation and feature stability analysis, inspiring ensemble methods like bagging and random forests, and informing uncertainty calibration for deep-learning predictions—thereby making cont...

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