33 Episodo

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

    Publicado: 23/11/2025

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

    Publicado: 3/11/2025

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

    Publicado: 19/9/2025

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

    Publicado: 26/7/2025

  5. Data Science #30 - The Bootstrap Method (1977)

    Publicado: 30/5/2025

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

    Publicado: 23/5/2025

  7. Data Science #28 - The Bloom filter algorithm

    Publicado: 23/5/2025

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

    Publicado: 2/4/2025

  9. Data Science #26 - The First Gradient decent algorithm by Cauchy (1847)

    Publicado: 23/3/2025

  10. Data Science #24 - The Expectation Maximization (EM) algorithm Paper review (1977)

    Publicado: 4/2/2025

  11. Data Science #23- The Markov Chain Monte Carl MCMC Paper review (1953)

    Publicado: 14/1/2025

  12. Data Science #22 - The theory of dynamic programming, Paper review 1954

    Publicado: 7/1/2025

  13. Data Science #21 - Steps Toward Artificial Intelligence

    Publicado: 25/12/2024

  14. Data Science #20 - the Rao-Cramer bound (1945)

    Publicado: 9/12/2024

  15. Data Science #19 - The Kullback–Leibler divergence paper (1951)

    Publicado: 2/12/2024

  16. Data Science #18 - The k-nearest neighbors algorithm (1951)

    Publicado: 25/11/2024

  17. Data Science #17 - The Monte Carlo Algorithm (1949)

    Publicado: 18/11/2024

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

    Publicado: 7/11/2024

  19. Data Science #15 - The First Decision Tree Algorithm (1963)

    Publicado: 28/10/2024

  20. Data Science #14 - The original k-means algorithm paper review (1957)

    Publicado: 10/10/2024

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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 podcast episodes are also available on our youtube: https://youtu.be/wThcXx_vXjQ?si=vnMfs

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