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S2E34 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 framework for training neural…
S2E33 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 nonlinear systems, drastically…
S2E32 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 connecting recurrence relations to…
S2E4 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 into simple diagrams and…
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 statistic’s sampling distribution…
S2E9 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-based partitioning method…
S2E8 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 allowing a small, controllable rate…
S2E7 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 the first formal…
S2E6 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 derivatives. Instead of elimination,…
S2E4 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." Journal of the royal…
S2E12 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 physics 21.6 (1953): 1087-1092…
S2E3 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 innovations. First, his Principle…
S2E1 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 of AI that outlines the…
S1E20 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 attainable in the estimation of…
S1E19 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 probability distributions,…
S1E20 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: Consistency Properties USAF…
S1E17 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 44.247 (1949): 335-341. The…
S1E16 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, Herbert and Sutton…
S1E15 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 key issues in analyzing…
S1E14 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 back to this paper. Loyd…
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Data Science Decoded has published 33 episodes since July 2024, covering topics in Mathematics, Science.
Data Science Decoded is currently dormant with new episodes every 2 weeks. Average episode length is 47m.
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