Castuner

Data Skeptic

Kyle Polich

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.

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Эпизоды

613 эпизодов

  1. Working in Extended Reality 39:16
  2. TastePrint 26:29
  3. Gestures Around the Ear 30:04
  4. Implicit Interactions 44:34
  5. The Lived Informatics Model 34:13
  6. Recommender Systems Today and Tomorrow 22:46
  7. Recommender Systems Optimization Goals 31:15
  8. Recommender Systems Origin Story 25:32
  9. Social Choice for Fair Recommendations 42:56
  10. News Recommendations 46:06
  11. Give Users the Wheel 35:28
  12. AutoLike 35:18
  13. Student Spotlight: Aaron Payne, Data Analyst 25:59
  14. The Future is Agentic in Recommender Systems 49:25
  15. Book Ratings and Recommendations 39:19
  16. Disentanglement and Interpretability in Recommender Systems 30:33
  17. Collective Altruism in Recommender Systems 54:35
  18. Niche vs Mainstream 34:10
  19. Healthy Friction in Job Recommender Systems 26:37
  20. Fairness in PCA-Based Recommenders 49:59
  21. Video Recommendations in Industry 38:16
  22. Eye Tracking in Recommender Systems 52:08
  23. Cracking the Cold Start Problem 39:57
  24. Designing Recommender Systems for Digital Humanities 36:48
  25. DataRec Library for Reproducible in Recommend Systems 32:48
  26. Shilling Attacks on Recommender Systems 34:48
  27. Music Playlist Recommendations 52:29
  28. Bypassing the Popularity Bias 34:33
  29. Sustainable Recommender Systems for Tourism 38:02
  30. Interpretable Real Estate Recommendations 32:57
  31. Why Am I Seeing This? 49:36
  32. Eco-aware GNN Recommenders 44:42
  33. Networks and Recommender Systems 17:45
  34. Network of Past Guests Collaborations 34:10
  35. The Network Diversion Problem 46:14
  36. Complex Dynamic in Networks 56:00
  37. Github Network Analysis 36:46
  38. Networks and Complexity 17:49
  39. Graphs for Causal AI 41:00
  40. Power Networks 41:50
  41. Unveiling Graph Datasets 44:12
  42. Network Manipulation 40:58
  43. The Small World Hypothesis 17:25
  44. Thinking in Networks 33:55
  45. Fraud Networks 42:55
  46. Criminal Networks 43:35
  47. Graph Bugs 29:01
  48. Organizational Network Analysis 44:15
  49. Organizational Networks 27:48
  50. Networks of the Mind 43:16