Machine Learning: How Did We Get Here?: Who It's For and Who Sponsors It

Est. audience: 100
Apple rating: 5 from 8
Cadence: Weekly
Latest: 2026-05-18
By: Tom Mitchell | Stanford Digital Economy Lab | Carnegie Mellon University
Based on: 12 recent episodes

Covers: Foundational history, research trajectories, algorithms, and key breakthroughs in machine learning told by pioneering computer scientists.
For: Machine learning researchers, AI engineers, computer scientists, and tech leaders.
Best for: AI professionals seeking historical insights and foundational technical perspectives from machine learning pioneers.
Skip if: General business operators looking for basic, hands-on software tutorials or sales advice.

Start with

Machine Learning and Speech Recognition with Kai-Fu Lee: Explores the development of speech recognition and early corporate machine learning efforts.
Decision Tree Learning with Ross Quinlan: Discusses algorithms like C4.5, ID3, and the commercial adoption of decision trees.
Reinforcement Learning with Rich Sutton: Explains foundational reinforcement learning concepts and why supervised learning alone is insufficient.

Summarised by AI from 12 recent episodes. Audience estimate and Apple rating from Podscan, checked 2026-11-03; treat the estimate as a guide and ask the show for its own numbers. Prices are not included. Report a mistake.