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PODCAST

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

science mathematics

All Episodes

00:46:05

Data Science #32 - A Markovian Decision Process,...

Data Science Decoded ·

2025/09/19

We reviewed Richard Bellman’s “A Markovian Decision Process” (1957), which introduced a mathematical framework for sequential decision-making under uncertainty. 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

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00:48:11

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

Data Science Decoded ·

2025/07/26

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.” 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

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00:41:05

Data Science #30 - The Bootstrap Method (1977)

Data Science Decoded ·

2025/05/30

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. Its ability to quantify uncertainty cheaply and flexibly underlies many staples of modern data science and AI,

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00:41:03

Data Science #29 - The Chi-square automatic...

Data Science Decoded ·

2025/05/23

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. Kass for exploring large categorical data sets by iteratively splitting records into mutually exclusive, exhaustive subsets based on the most statistically significant predictors rather than maximal explanatory power. Unlike its predecessor, AID, CHAID embeds each split in a chi-squared

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00:39:15

Data Science #28 - The Bloom filter algorithm

Data Science Decoded ·

2025/05/23

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 positives.Unlike traditional methods that store full data, Bloom filters use a compact bit array and multiple hash functions, trading exactness for speed and memory savings. This idea transformed modern data science and big data systems, powering tools like Apache Spark, Cassandra, and Kafka,

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00:32:09

Data Science #27 - The History of Least Squares (1877)

Data Science Decoded ·

2025/04/02

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 Gauss (1809) for linking it to the normal distribution. He evaluates multiple proofs, including Laplace’s (1810) general probability-based derivation, and highlights later refinements by various mathematicians. The paper underscores the method’s fundamental role in statistical

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00:33:14

Data Science #26 - The First Gradient decent...

Data Science Decoded ·

2025/03/23

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 progressively reducing the function’s value through small updates, forming an early version of gradient descent. His approach allowed systematic approximation of solutions, influencing numerical optimization.This work laid the foundation for machine learning and AI, where gradient-based

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00:32:47

Data Science #24 - The Expectation Maximization (EM)...

Data Science Decoded ·

2025/02/04

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 B (methodological) 39.1 (1977): 1-22. The Expectation-Maximization (EM) algorithm is an iterative method for finding Maximum Likelihood Estimates (MLEs) when data is incomplete or contains latent variables. It alternates between the E-step, where it computes the expected

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00:37:54

Data Science #23- The Markov Chain Monte Carl MCMC...

Data Science Decoded ·

2025/01/14

In the 23rd episode we review the The 1953 paper Metropolis, Nicholas, et al. "Equation of state calculations by fast computing machines." The journal of chemical physics 21.6 (1953): 1087-1092 which introduced the Monte Carlo method for simulating molecular systems, particularly focusing on two-dimensional rigid-sphere models. The study used random sampling to compute equilibrium properties like pressure and density, demonstrating a feasible approach for solving analytically intractable

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00:47:46

Data Science #22 - The theory of dynamic programming,...

Data Science Decoded ·

2025/01/07

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 established that optimal solutions have a recursive structure - each sub-decision must be optimal given the state resulting from previous decisions. Second, he introduced the concept of focusing on immediate states rather than complete historical sequences, providing a practical way to

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00:59:39

Data Science #21 - Steps Toward Artificial Intelligence

Data Science Decoded ·

2024/12/25

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 methodologies for developing intelligent problem-solving systems. The paper categorizes AI challenges into five key areas: Search, Pattern Recognition, Learning, Planning, and Induction. It emphasizes how computers, limited by their ability to perform only programmed actions, can enhance

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00:59:42

Data Science #20 - the Rao-Cramer bound (1945)

Data Science Decoded ·

2024/12/09

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". Bulletin of the Calcutta Mathematical Society. 37. Calcutta Mathematical Society: 81–89. The Cramér-Rao Bound (CRB) sets a theoretical lower limit on the variance of any unbiased estimator for a parameter. It is derived from the Fisher information, which quantifies how much the

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00:52:41

Data Science #19 - The Kullback–Leibler divergence...

Data Science Decoded ·

2024/12/02

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 of assuming one distribution when another is true. This concept, rooted in Shannon's information theory (which we reviewed in previous episodes), became fundamental in hypothesis testing, model evaluation, and statistical inference. KL divergence has profoundly impacted data science

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00:44:01

Data Science #18 - The k-nearest neighbors algorithm...

Data Science Decoded ·

2024/11/25

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 Medicine, Randolph Field, Texas They introduces a nonparametric method for classifying a new observation 𝑧 z as belonging to one of two distributions, 𝐹 F or 𝐺 G, without assuming specific parametric forms. Using 𝑘 k-nearest neighbor density estimates, the paper implements a

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00:38:11

Data Science #17 - The Monte Carlo Algorithm (1949)

Data Science Decoded ·

2024/11/18

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 uses random sampling to approximate solutions for problems that are too complex for analytical methods, such as integration, optimization, and simulation. Its power lies in leveraging randomness to solve high-dimensional and nonlinear problems, making it a fundamental tool in

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00:42:20

Data Science #16 - The First Stochastic Descent...

Data Science Decoded ·

2024/11/07

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 the stochastic approximation method, a groundbreaking iterative technique for finding the root of an unknown function using noisy observations. This method enabled real-time, adaptive estimation without requiring the function’s explicit form, revolutionizing statistical practices in

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00:36:35

Data Science #15 - The First Decision Tree Algorithm...

Data Science Decoded ·

2024/10/28

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, such as high dimensionality, categorical variables, measurement errors, sample variability, intercorrelations, interaction effects, and causal chains. These challenges complicate efforts to draw meaningful conclusions about relationships between factors like income, education, and

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00:46:57

Data Science #14 - The original k-means algorithm...

Data Science Decoded ·

2024/10/10

At the 14th episode we go over the Stuart Lloyd's 1957 paper, "Least Squares Quantization in PCM," (which was published only at 1982)The k-means algorithm can be traced back to this paper. Loyd introduces an approach to quantization in pulse-code modulation (PCM). Which is like a 1-D k means clustering. Lloyd discusses how quantization intervals and corresponding quantum values should be adjusted based on signal amplitude distributions to minimize noise, improving efficiency in PCM systems

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00:29:25

Data Science #13 - Kolmogorov complexity paper review...

Data Science Decoded ·

2024/10/01

In the 14th episode we review the second part of Kolmogorov's seminal paper:Three approaches to the quantitative definition of information’." Problems of information transmission 1.1 (1965): 1-7. The paper introduces algorithmic complexity (or Kolmogorov complexity), which measures the amount of information in an object based on the length of the shortest program that can describe it. This shifts focus from Shannon entropy, which measures uncertainty probabilistically, to understanding the

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00:38:53

Data Science #12 - Kolmogorov complexity paper review...

Data Science Decoded ·

2024/09/28

In the 12th episode we review the first part of Kolmogorov's seminal paper: "3 approaches to the quantitative definition of information’." Problems of information transmission 1.1 (1965): 1-7. The paper introduces algorithmic complexity (or Kolmogorov complexity), which measures the amount of information in an object based on the length of the shortest program that can describe it. This shifts focus from Shannon entropy, which measures uncertainty probabilistically, to understanding the

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