Data Science Decoded

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

On the 34th episode, we review the 1986 paper, "Learning representations by back-propagating errors" , which was pivotal because it provided a clear, generalized framewor

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

On the 33rd episdoe we review Paul Werbos’s “Applications of Advances in Nonlinear Sensitivity Analysis” which presents efficient methods for computing derivatives in non

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

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

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

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

Data Science #30 - The Bootstrap Method (1977)

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 statis

Data Science #29 - The Chi-square automatic interaction detection(CHAID) algorithm (1979)

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-bas

Data Science #28 - The Bloom filter algorithm

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 allowin

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

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

Data Science #26 - The First Gradient decent algorithm by Cauchy (1847)

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 derivat

Data Science #24 - The Expectation Maximization (EM) algorithm Paper review (1977)

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." Jo

Data Science #23- The Markov Chain Monte Carl MCMC Paper review (1953)

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 physi

Data Science #22 - The theory of dynamic programming, Paper review 1954

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 inno

Data Science #21 - Steps Toward Artificial Intelligence

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 o

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

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 attainab

Data Science #19 - The Kullback–Leibler divergence paper (1951)

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 p

Data Science #18 - The k-nearest neighbors algorithm (1951)

In the 18th episode we go over the original k-nearest neighbors algorithm; Fix, Evelyn; Hodges, Joseph L. (1951). Discriminatory Analysis. Nonparametric Discrimination:

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

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

Data Science #16 - The First Stochastic Descent Algorithm (1952)

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,

Data Science #15 - The First Decision Tree Algorithm (1963)

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

Data Science #14 - The original k-means algorithm paper review (1957)

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