Pranesh

“Unfortunately, creating an objective function that matches the true goal of the data mining is usually impossible, so data scientists often choose based on faith[22] and experience.”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“In a different direction, the necessity to model the analysis of noisy incomplete sensory data not by logic but by Bayesian inference first came to the forefront in the robotics community with their use of Kalman filters.”
― Ulf Grenander, Calculus Of Ideas, A: A Mathematical Study Of Human Thought

“How do we learn? Is there a better way? What can we predict? Can we trust what we’ve learned? Rival schools of thought within machine learning have very different answers to these questions. The main ones are five in number, and we’ll devote a chapter to each. Symbolists view learning as the inverse of deduction and take ideas from philosophy, psychology, and logic. Connectionists reverse engineer the brain and are inspired by neuroscience and physics. Evolutionaries simulate evolution on the computer and draw on genetics and evolutionary biology. Bayesians believe learning is a form of probabilistic inference and have their roots in statistics. Analogizers learn by extrapolating from similarity judgments and are influenced by psychology and mathematical optimization.”
― Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

“For Wiener, entropy was a measure of disorder; for Shannon, of uncertainty. Fundamentally, as they were realizing, these were the same.”
― James Gleick, The Information: A History, a Theory, a Flood