ON MEASURING MACHINE LEARNING MODELS AGAINST CONCRETE BUSINESS OBJECTIVES

REVIEW NOTES: DATA SCIENCE FOR BUSINESS BY PROVOST & FAWCETT: CHAPTER 7

I enjoyed reading this chapter. It’s insightful and well explained with detailed examples, diagrams and graphics, on a few data science topics that correspond directly to conventional scientific research in computer science. That makes me happy, because these are crucial points, yet rarely are the focus of Kaggle Competitions, books on Machine Learning or Statistics, the latest and greatest in TensorFlow, PyTorch, AutoML libraries (etc, etc) and too infrequently discussed in DL/AI/ML social posts and blogs. Below I have written about the points that are well worth taking home. These topics are broadly on:

  • Careful consideration of what is desired from data science results.
  • Expected value as a key evaluation framework.
  • Consideration of appropriate comparative baselines, in machine learning models.
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Book review 2/2 on Robot Proof: Higher Education in the Age of AI

I finished the book by Joseph Aoun a little while ago, and I’ve been sitting on my notes letting them stir. I think i have a fairly safe conclusion for its second half. That said, I would expect those with an understanding and empathetic relationship with their CS students and their families will have been at the cusp of some similar conclusions drawn by Aoun in Robot Proof in 2017.

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