Issue #102
May 9, 2021
This weeks Data Science Book is " Data Analysis: A Bayesian Tutorial " by D. S. Sivia and J. Skilling. Bayesian analysis is a statistical approach with a long and rich history that allows us to use probability statements to quantify our uncertainty about specific parameters. This short book provides an excellent first introduction to this powerful family of techniques with practical examples. The book quickly guides us from the fundamental intuition behind Bayes theorem more advanced concepts and applications such as Model comparison, Inference and Non-Parametric Estimation.
- 1. Game theory as an engine for large-scale data analysis [deepmind.com]
- 2. Automata Modulo Theories [cacm.acm.org]
- 3. Why Physics has Relevancy To Artificial Intelligence And Building AI Leadership Brain Trust? [forbes.com]
- 4. The Pastry A.I. That Learned to Fight Cancer [newyorker.com]
- 5. National Artificial Intelligence Initiative [ai.gov]
- 6. Kolmogorov Complexity [blog.neotree.uber.space]
- 7. The Computers Are Getting Better at Writing [newyorker.com]
- 8. Diffie-Hellman for the Layman [borisreitman.medium.com]
- 9. Principal Component Analysis Explained Visually [setosa.io]
- 10. Andrew Ng X-Rays the AI Hype [spectrum.ieee.org]
- 11. A Hitchhiker's Guide to SQLite with Python [arctype.com]
- • Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios — United States, April–September 2021 (R. K. Borchering, C. Viboud, E. Howerton, C. P. Smith, S. Truelove, M. C. Runge, N. G. Reich, L. Contamin, J. Levander, et al)
- • Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data (A. Barrat, C. Cattuto, M. Kivelä, S. Lehmann, J. Saramäki)
- • Network medicine framework for identifying drug-repurposing opportunities for COVID-19 (D. M. Gysi, Í. do Valle, M. Zitnik, A. Ameli, X. Gan, O. Varol, S. D. Ghiassian, J. J. Patten, R. A. Davey, J. Loscalzo, A.-L. Barabási)
- • Global inequality remotely sensed (M. U. Mirza, C. Xu, B. van Bavel, E. H. van Nes, M. Scheffer)
- • Machine Learning in Causal Inference: How do I love thee? Let me count the ways (L. B. Balzer, M. L. Petersen)
- • Characterizing key agents in the cryptocurrency economy through blockchain transaction analysis (X. F. Liu, H.-H. Ren, S.-H. Liu, X.-J. Jiang)
- • Building surrogate temporal network data from observed backbones (C. Presigny, P. Holme, A. Barrat)
- • Centralities in complex networks (A. Bovet, H. A. Makse)
- • Capturing the diversity of multilingual societies (T. Louf, D. Sanchez, J. J. Ramasco)
- • Understanding bias in facial recognition technologies (D. Leslie)
Decision Trees in Python from Start to Finish
All our videos are also available in our YouTube playlist.
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