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ddarmon.github.io | ||
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minireference.com
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| | | | | [AI summary] The author critiques the outdated, formula-heavy introductory statistics curriculum and outlines a plan for a new textbook that prioritizes practical skills, randomization methods, and a deeper conceptual understanding over rote memorization of analytical approximations. | |
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brenocon.com
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| | | | | [AI summary] The provided text is a collection of comments and discussions from a blog post that originally criticized artificial neural networks (ANNs) in 2008. The comments reflect a range of opinions and debates about the relationship between machine learning (ML) and statistics, with some users defending ML techniques like support vector machines (SVMs), probabilistic graphical models, and deep learning, while others argue for the importance of statistical methods. There are also discussions about the need for better communication between disciplines, the limitations of ML approaches, and the importance of understanding the underlying assumptions of models. The text includes recommendations for textbooks and courses, such as 'All of Statistics' and Andre... | |
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debrouwere.org
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| | | | | [AI summary] A data scientist argues that traditional descriptive statistics like the mean and standard deviation are often poor choices for communicating data and recommends more interpretable alternatives like medians, percentiles, and visualizations. | |
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fodsi.us
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| | | [AI summary] The ML4A Virtual Workshop explores how machine learning enhances classical algorithms through data-driven approaches, featuring talks on deep generative models, model-based deep learning, and learning-augmented algorithms. | ||
