- Home
- Podcasts
- Data Skeptic
Data Skeptic
Data scientists, machine learning engineers, and researchers applying critical thinking to technology.
- Cadence
- Every two weeks
- Latest
- 2026-10-29
- By
- Kyle Polich
- Based on
- 12 recent episodes
- Covers: Data science, statistics, machine learning, artificial intelligence, recommender system architectures, and human-computer interaction.
- For: Data scientists, machine learning engineers, and researchers applying critical thinking to technology.
- Best for: Technical professionals and engineers evaluating algorithms, recommender systems, and AI research.
- Skip if: General business leaders looking for non-technical sales or marketing tips.
Start with
- Recommender Systems Optimization Goals: Explores how to balance engagement, fairness, popularity bias, and embeddings in recommender algorithms.
- News Recommendations: Covers responsible AI, filter bubbles, and using the NewsRecLib framework to evaluate systems.
- AutoLike: Explains using reinforcement learning frameworks to audit hidden personalization algorithms on content platforms.
Visit
Summarised by AI from 12 recent episodes. Audience size is not included. Prices are not included. Report a mistake.