Where can I listen to this podcast?

This podcast is available on 7 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.

Data Science Decoded

Claim This Podcast

by Mike E

33 episodes

Updated Weekly

Accepts GuestsHas Sponsors

Podcast Authority

Beta

FairBased on show quality, social media presence, reviews, charts, and more

Quality52

Social0

YouTube85

Engagement31

Learn more

Podcast Overview

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

Language

🇺🇲

Publishing Since

7/7/2024

Visit Website View on Apple Podcasts RSS Feed

🇺🇸 Charts #3

Outreach generated

Apple 142

Spotify 827

Instant mention tracking

Perfect fit!

Unlock The Full Podcast Authority Score Report

See how your podcast performs across key metrics

Podcast Authority

Beta

FairBased on show quality, social media presence, reviews, charts, and more

Quality52

Social0

YouTube85

Engagement31

Learn more

Excellent Areas

Good Performance

Growth Opportunities

excellent

Episode Length

40 minutes

Performing excellently!

good

Publishing Consistency

Every 13 days

Recommendations available

Unlock the full report to see detailed tips

poor

Episode Thumbnails

Recommendations available

Unlock the full report to see detailed tips

+16 More Metrics

Unlock comprehensive insights including:

• YouTube presence analysis
• Social media reach metrics
• RSS compliance scoring
• Podcast 2.0 features
• Technical standards

What's Included in Your Full Report

Detailed Analytics

Complete breakdown of all 19 authority metrics
Personalized recommendations for each metric
Industry benchmarks and comparisons
Technical RSS feed analysis and compliance scoring

Growth Strategies

Step-by-step action plans for improvement
Quick wins to boost your score immediately
Pro tips from successful podcasters

Get your free podcast insights report

See how your show performs across every key metric

Instant delivery

No spam

Attract Better Guests

High authority scores make your podcast more attractive to industry leaders and influencers who want to appear on credible shows.

Secure Sponsorships

Sponsors look for podcasts with proven authority and engagement. Your score demonstrates your podcast's value to potential partners.

Grow Your Audience

Understanding your strengths and weaknesses helps you make data-driven decisions to expand your listener base effectively.

1 verified contact email on file for Data Science Decoded

Pitch yourself as a guest, propose sponsorships, or reach out directly to the host.

See contact details

Recent Episodes

November 23, 2025

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

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

November 3, 2025

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

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.

September 19, 2025

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

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

33 total episodes available

Similar Podcasts

Discover related shows you might enjoy

Darknet Diaries

Jack Rhysider

Deep-dive analytics for Data Science Decoded

Frequently asked questions

Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.

What is 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 podcast episodes are also available on our youtube: https://youtu.be/wThcXx_vXjQ?si=vnMfs
How often does this podcast release new episodes?: This podcast updates weekly.
Where can I listen to this podcast?: This podcast is available on 7 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
Does this podcast accept guests?: Information about guest appearances is not available.

Legal Disclaimer

Pod Engine is not affiliated with, endorsed by, or officially connected with any of the podcasts displayed on this platform. We operate independently as a podcast discovery and analytics service.

All podcast artwork, thumbnails, and content displayed on this page are the property of their respective owners and are protected by applicable copyright laws. This includes, but is not limited to, podcast cover art, episode artwork, show descriptions, episode titles, transcripts, audio snippets, and any other content originating from the podcast creators or their licensors.

We display this content under fair use principles and/or implied license for the purpose of podcast discovery, information, and commentary. We make no claim of ownership over any podcast content, artwork, or related materials shown on this platform. All trademarks, service marks, and trade names are the property of their respective owners.

While we strive to ensure all content usage is properly authorized, if you are a rights holder and believe your content is being used inappropriately or without proper authorization, please contact us immediately at hey@podengine.ai for prompt review and appropriate action, which may include content removal or proper attribution.

By accessing and using this platform, you acknowledge and agree to respect all applicable copyright laws and intellectual property rights of content owners. Any unauthorized reproduction, distribution, or commercial use of the content displayed on this platform is strictly prohibited.