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We discuss seminal mathematical papers (sometimes really old ð ) 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
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
āļāļđāđāļāļąāļ:
āļāļ§āļēāļĄāļāļĩāđāđāļāļāļēāļĢāļāļąāļāđāļāļ:
monthly
āļāļ§āļēāļĄāļĒāļēāļ§āđāļŠāļĩāļĒāļāđāļāļĒāđāļāļĨāļĩāđāļĒ:
48 minutes
āļ āļēāļĐāļēāļāļąāļāļāļĪāļĐ
āļŠāļŦāļĢāļąāļāļāđāļĄāļĢāļīāļāļē
34 āļāļāļ
āļāļąāđāļāđāļāđ 7 āļāļĢāļāļāļēāļāļĄ 2024
episodic
āđāļāļāļāļāđāļāļŠāļāđāļāļĩāđ 🔗
āļāļāļāļĨāđāļēāļŠāļļāļ 🔗
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 âĶ
āļāđāļāļŦāļēāļāļāļāļāļĩāđāļāđāļēāļāļĄāļē
āļāđāļāļŦāļēāļāļāļāļāļĩāđāļāđāļēāļāļĄāļēāļāļāļ Data Science Decoded
āļāļāļāļāđāļāļāļŦāļāđāļēāļāļĩāđ 🔗
On the 33rd episdoe we review Paul Werbosâs âApplications of Advances in Nonlinear Sensitivity Analysisâ which presents efficient methods for computing derivatives in nonlinear systems, drastically reducing computational costs for large-scale models.
Werbos, Paul J. "Applications of advancesâĶ
We reviewed Richard Bellmanâs âA Markovian Decision Processâ (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 prinâĶ
On the 31st episode of the podcast, we add Liron to the team, we review a gem from 1921, where SewallâŊWright 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 âpath coefficients.â
By tâĶ
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 statisticâs sampling distribution by repeatedly drawing with replacement from the observed data, allowing estimation of standard errorsâĶ
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 mâĶ
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 âĶ
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,âĶ
In this episode, we review Cauchyâs 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 functionâs value through small updates, forming an early verâĶ
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 iâĶ
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If I have seen further than others, it is by listening to podcasts and standing upon the shoulders of
giants.
‐ Isaac "Llamacorn" Newton
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