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

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 weights of connections in the network to minimize the error between the actual and desired output vectors. Crucially, this process forces the hidden units, whose desired states aren't specified, to develop distributed internal representations of the task domain's important features.This capability to construct useful new features distinguishes back-propagation from earlier, simpler methods like the perceptron-convergence procedure. The authors demonstrate its power on non-trivial problems, such as detecting mirror symmetry in an input vector and storing information about isomorphic family trees. By showing how the network generalizes correctly from one family tree to its Italian equivalent, the paper illustrated the algorithm's ability to capture the underlying structure of the task domain.Despite recognizing that the procedure was not guaranteed to find a global minimum due to local minima in the error-surface , the paper's clear formulation (using equations 1-9 ) and its successful demonstration of learning complex, non-linear representations served as a powerful catalyst. It fundamentally advanced the field of connectionism and became the standard, foundational algorithm used today to train multi-layered networks, or deep learning models, despite the earlier, lesser-known work by Werbos

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 reducing computational costs for large-scale models. Werbos, Paul J. "Applications of advances in nonlinear sensitivity analysis." System Modeling and Optimization: Proceedings of the 10th IFIP Conference New York City, USA, August 31–September 4, 1981These methods, especially the backward differentiation technique, enable better sensitivity analysis, optimization, and stochastic modeling across economics, engineering, and artificial intelligence. The paper also introduces Generalized Dynamic Heuristic Programming (GDHP) for adaptive decision-making in uncertain environments.Its importance to modern data science lies in laying the foundation for backpropagation, the core algorithm behind training neural networks. Werbos’s work bridged traditional optimization and today’s AI, influencing machine learning, reinforcement learning, and data-driven modeling.

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 Processes (MDPs), which underpin algorithms like value iteration, policy iteration, and Q-learning. From robotics to large-scale systems like AlphaGo, nearly all of RL traces back to the foundations Bellman set in 1957

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 matrix—turning the slogan “correlation ≠ causation” into a workable calculus for observational data.Wright’s algebra and diagrams became the blueprint for modern graphical causal models, structural‑equation modelling, and DAG‑based inference that power libraries such as DoWhy, Pyro and CausalNex. The same logic underlies feature‑importance decompositions, counterfactual A/B testing, fairness audits, and explainable‑AI tooling, making a century‑old livestock‑breeding study a foundation stone of present‑day data‑science and AI practice.

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, powering model evaluation and feature stability analysis, inspiring ensemble methods like bagging and random forests, and informing uncertainty calibration for deep-learning predictions—thereby making contemporary models more reliable and robust.Efron, B. "Bootstrap methods: Another look at the bootstrap." The Annals of Statistics 7 (1977): 1-26.

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 significance test (with Bonferroni‐corrected thresholds), allows multi-way divisions, and handles missing or “floating” categories gracefully.In practice, CHAID proceeds by merging predictor categories that are least distinguishable (stepwise grouping) and then testing whether any compound categories merit a further split, ensuring parsimonious, stable groupings without overfitting. Through its significance‐driven, multi-way splitting and built-in bias correction against predictors with many levels, CHAID yields intuitive decision trees that highlight the strongest associations in high-dimensional categorical data In modern data science, CHAID’s core ideas underpin contemporary decision‐tree algorithms (e.g., CART, C4.5) and ensemble methods like random forests, where statistical rigor in splitting criteria and robust handling of missing data remain critical. Its emphasis on automated, hypothesis‐driven partitioning has influenced automated feature selection, interpretable machine learning, and scalable analytics workflows that transform raw categorical variables into actionable insights.

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, where fast filtering and memory efficiency are critical for performance at scale.

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 estimation, probability theory, and error minimization, solidifying its place in scientific and engineering applications.

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 methods are essential. Modern stochastic gradient descent (SGD) and deep learning training algorithms follow Cauchy’s principle of stepwise minimization. His ideas power optimization in neural networks, making AI training efficient and scalable.

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 value of the missing data given current parameter estimates, and the M-step, where it maximizes the expected complete-data log-likelihood to update the parameters. This process repeats until convergence, ensuring a monotonic increase in the likelihood function.EM is widely used in statistics and machine learning, especially in Gaussian Mixture Models (GMMs), hidden Markov models (HMMs), and missing data imputation. Its ability to handle incomplete data makes it invaluable for problems in clustering, anomaly detection, and probabilistic modeling. The algorithm guarantees stable convergence, though it may reach local maxima, depending on initialization.In modern data science and AI, EM has had a profound impact, enabling unsupervised learning in natural language processing (NLP), computer vision, and speech recognition. It serves as a foundation for probabilistic graphical models like Bayesian networks and Variational Inference, which power applications such as chatbots, recommendation systems, and deep generative models. Its iterative nature has also inspired optimization techniques in deep learning, such as Expectation-Maximization inspired variational autoencoders (VAEs), demonstrating its ongoing influence in AI advancements.