Data Science Decoded
- Science · Mathematics
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About
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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Recent episodes
- Data Science #34 - The deep learning original paper review, Hinton, Rumelhard & Williams (1985)
- Data Science #33 - The Backpropagation method, Paul Werbos (1980)
- Data Science #32 - A Markovian Decision Process, Richard Bellman (1957)
- Data Science #31 - Correlation and causation (1921), Wright Sewall
- Data Science #30 - The Bootstrap Method (1977)
- Data Science #29 - The Chi-square automatic interaction detection(CHAID) algorithm (1979)
- Data Science #28 - The Bloom filter algorithm
- Data Science #27 - The History of Least Squares (1877)
- Data Science #26 - The First Gradient decent algorithm by Cauchy (1847)
- Data Science #24 - The Expectation Maximization (EM) algorithm Paper review (1977)
