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PODCAST

Data Science Decoded

We discuss seminal mathematical papers (sometimes really old 😎 ) 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

science mathematics

All Episodes

00:38:53

Data Science #12 - Kolmogorov complexity paper review...

Data Science Decoded ·

2024/09/28

In the 12th episode we review the first part of Kolmogorov's seminal paper: "3 approaches to the quantitative definition of information’." Problems of information transmission 1.1 (1965): 1-7. The paper introduces algorithmic complexity (or Kolmogorov complexity), which measures the amount of information in an object based on the length of the shortest program that can describe it. This shifts focus from Shannon entropy, which measures uncertainty probabilistically, to understanding the

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01:03:29

"The Perceptron: A Probabilistic Model for...

Data Science Decoded ·

2024/09/20

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.Rosenblatt explores two theories: one where sensory data is stored as coded representations, and another, which he advocates, where learning occurs through forming new neural connections. The perceptron illustrates this connectionist approach

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00:55:41

The original principal component analysis (PCA)...

Data Science Decoded ·

2024/09/12

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. The foundational concepts of eigenvalue decomposition and maximizing variance in orthogonal directions form the backbone of PCA, which is now

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01:24:32

"The unreasonable effectiveness of mathematics in the...

Data Science Decoded ·

2024/09/10

In this special episode, Daniel Aronovich joins forces with the 632 nm podcast.In this timeless paper Wigner reflects on how mathematical concepts, often developed independently of any concern for the physical world, turn out to be remarkably effective in describing natural phenomena. This effectiveness is "unreasonable" because there is no clear reason why abstract mathematical constructs should align so well with the laws governing the universe. Full paper is at our

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00:54:57

The Turing test by Alan Turing, "Computing machinery...

Data Science Decoded ·

2024/09/04

This paper is a foundational text in the field of artificial intelligence (AI) and explores the question: "Can machines think?" Turing introduces what is now known as the "Turing Test" as a way to operationalize this question, he called it the imitation game. Are there imaginable digital computers that could perform well in the imitation game? The imitation game involves an interrogator trying to distinguish between a human and a machine based on their responses to various

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00:47:30

"The use of multiple measurements in taxonomic...

Data Science Decoded ·

2024/08/12

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

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00:56:32

On the problem of the most efficient tests of...

Data Science Decoded ·

2024/08/07

This paper is considered one of the foundational works in modern statistical hypothesis testing.Key insights and influences: Neyman-Pearson Lemma: The paper introduced the Neyman-Pearson Lemma, which provides a method for finding the most powerful test for a simple hypothesis against a simple alternative. Type I and Type II errors: It formalized the concepts of Type I (false positive) and Type II (false negative) errors in hypothesis testing. Power of a test: The paper introduced the concept of

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01:07:22

Data Science Decoded - "A Mathematical Theory of...

Data Science Decoded ·

2024/07/30

Shannon, Claude Elwood. "A mathematical theory of communication." The Bell system technical journal 27.3 (1948): 379-423.Part 3/3. The paper fundamentally reshapes how we understand communication. The paper introduces a formal framework for analyzing communication systems, addressing the transmission of information with and without noise. Key concepts include the definition of information entropy, the logarithmic measure of information, and the capacity of communication channels. In

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00:41:11

Data Science Decoded - "A Mathematical Theory of...

Data Science Decoded ·

2024/07/21

Shannon, Claude Elwood. "A mathematical theory of communication." The Bell system technical journal 27.3 (1948): 379-423.Part 2/3. The paper fundamentally reshapes how we understand communication. The paper introduces a formal framework for analyzing communication systems, addressing the transmission of information with and without noise. Key concepts include the definition of information entropy, the logarithmic measure of information, and the capacity of communication channels

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00:41:04

Data Science Decoded: "A Mathematical Theory of...

Data Science Decoded ·

2024/07/16

Shannon, Claude Elwood. "A mathematical theory of communication." The Bell system technical journal 27.3 (1948): 379-423. Part 1/3. The paper fundamentally reshapes how we understand communication. The paper introduces a formal framework for analyzing communication systems, addressing the transmission of information with and without noise. Key concepts include the definition of information entropy, the logarithmic measure of information, and the capacity of communication channels

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01:01:29

Data Science Decoded - "Application of the Logistic...

Data Science Decoded ·

2024/07/07

"Application of the Logistic Function to Bio-Assays" (1944), Berkson Joseph It gained further prominence in the 20th century through applications in various fields, including biology and bio-assay. Joseph Berkson's 1944 paper, 'Application of the Logistic Function to Bio-Assay,' was pivotal in popularizing its use for estimating drug potency. Berkson argued that the logistic function was a more statistically manageable and theoretically sound alternative to the probit

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01:16:59

Data Science Decoded - Fisher RA. "On the...

Data Science Decoded ·

2024/07/07

We discuss Ronald A. Fisher's paper "On the Mathematical Foundations of Theoretical Statistics"(1922), which profoundly shaped the field of statistics by establishing key concepts such as maximum likelihood estimation, which is crucial for parameter estimation in statistical models.

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