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By Mike E
The podcast currently has 12 episodes available.
In the 12th episode we review the first part of Kolmogorov's seminal paper:
Kolmogorov argues that systems like texts or biological data, governed by rules and patterns, are better analyzed by their compressibility—how efficiently they can be described—rather than by random probabilistic models.
In AI, tasks such as text generation and data compression directly apply Kolmogorov's concept of finding the most compact representation, making his work foundational for building efficient, powerful models.
Frank Rosenblatt's 1958 paper, "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain," introduces the perceptron, an early neural network model inspired by how the brain stores and processes information.
The perceptron illustrates this connectionist approach by mimicking how neurons process input and reinforce connections based on experience.
Though limited in handling complex, non-linear data, the perceptron established key principles—such as weighted connections and learning from data.
Hotelling, Harold. "Analysis of a complex of statistical variables into principal components." Journal of educational psychology 24.6 (1933): 417.
This seminal work by Harold Hotelling on PCA remains highly relevant to modern data science because PCA is still widely used for dimensionality reduction, feature extraction, and data visualization.
In this special episode, Daniel Aronovich joins forces with the 632 nm podcast.
This effectiveness is "unreasonable" because there is no clear reason why abstract mathematical constructs should align so well with the laws governing the universe.
https://datasciencedecodedpodcast.com/episode-9-the-unreasonable-effectiveness-of-mathematics-in-natural-sciences-eugene-wigner-1960
This paper is a foundational text in the field of artificial intelligence (AI) and explores the question: "Can machines think?"
Turing addresses each objection, ultimately suggesting that machines can indeed be said to think if they can perform human-like tasks, especially those that involve reasoning, learning, and language.
This paper introduced linear discriminant analysis(LDA), a statistical technique that revolutionized classification in biology and beyond.
Fisher demonstrated how to use multiple measurements to distinguish between different species of iris flowers, laying the foundation for modern multivariate statistics.
His work showed that combining several characteristics could provide more accurate classification than relying on any single trait.
This paper not only solved a practical problem in botany but also opened up new avenues for statistical analysis across various fields.
Fisher's method became a cornerstone of pattern recognition and machine learning, influencing diverse areas from medical diagnostics to AI.
The iris dataset he used, now known as the "Fisher iris" or "Anderson iris" dataset, remains a popular example in data science education and research.
This paper is considered one of the foundational works in modern statistical hypothesis testing.
Key insights and influences:
These concepts have had a profound influence on modern statistical theory and practice, forming the basis of much of classical hypothesis testing used today in various fields of science and research.
Shannon, Claude Elwood. "A mathematical theory of communication." The Bell system technical journal 27.3 (1948): 379-423.
Shannon, Claude Elwood. "A mathematical theory of communication." The Bell system technical journal 27.3 (1948): 379-423.
Shannon, Claude Elwood. "A mathematical theory of communication." The Bell system technical journal 27.3 (1948): 379-423.
The podcast currently has 12 episodes available.