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

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 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 fields like bioassay and engineering. Robbins and Monro’s work laid the ideas behind stochastic gradient descent (SGD), the primary optimization algorithm in modern machine learning and deep learning. SGD’s efficiency in training neural networks through iterative updates is directly rooted in this method.


Additionally, their approach to handling binary feedback inspired early concepts in reinforcement learning, where algorithms learn from sparse rewards and adapt over time. The paper's principles are fundamental to nonparametric methods, online learning, and dynamic optimization in data science and AI today.


By enabling sequential, probabilistic updates, the Robbins-Monro method supports adaptive decision-making in real-time applications such as recommender systems, autonomous systems, and financial trading, making it a cornerstone of modern AI’s ability to learn in complex, uncertain environments.

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(33)

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

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 networks...

23 Nov 202546min

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

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 re...

3 Nov 202557min

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

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 M...

19 Sep 202546min

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

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 provi...

26 Jul 202548min

Data Science #30 - The Bootstrap Method (1977)

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 by...

30 Mai 202541min

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

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 introdu...

23 Mai 202541min

Data Science #28 - The Bloom filter algorithm

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 o...

23 Mai 202539min

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

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 probabilistic...

2 Apr 202532min

Populært innen Vitenskap

fastlegen
tingenes-tilstand
abels-tarn
romkapsel
jss
liberal-halvtime
vett-og-vitenskap-med-gaute-einevoll
rekommandert
sinnsyn
dekodet-2
rss-rekommandert
villmarksliv
fjellsportpodden
rss-overskuddsliv
tomprat-med-gunnar-tjomlid
rss-inn-til-kjernen-med-sunniva-rose
kvinnehelsepodden
rss-paradigmepodden
diagnose
rss-nysgjerrige-norge