Top 10 Signs of the Textpocalypse
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odscHuman-generated text may be the next frontier for big data analysis, but we humans are complicated beasts and the text we generate is messy and complicated in ways that can confound analysis. We’ll describe the top ten mistakes people make when they start doing text analysis, and hopefully save you from making a few of these mistakes yourself.
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Peter Bull, co-founder at DrivenData, will speak on the ways in which statistics, computer science, and machine learning can be applied to the challenges in the social sector. The talk will address both the big-picture context of the data for good movement, and an in-depth case study of the methods which won DrivenData’s recent machine learning competitions.
It’s an exciting time for people who love data: methods are improving, computational costs are decreasing, storage and transport are cheaper, and the talent pool is growing. It’s up to the data geeks to use these powers for good.
Machine Learning for Suits
You will learn the basic concepts of machine learning – such as Modeling, Model Selection, Loss or Profit, overfitting, and validation – in a non-mathematical way, so that you can ask for data analysis and interpret the results of a model in the context of making business decisions. The concepts behind machine learning are actually quite simple, so expect to take away not just words and acronyms, but rather, a deep understanding. We will work in the context of concrete examples from different domains, including finance and medicine.
What is probability? What is a model? Supervised vs unsupervised learning. Regression and Classification.
Models and Data: Bias, Variance, Noise, Overfitting, and how to solve Overfitting with Regularization and Validation
Risk and Cost, Data snooping, asymmetry in data, experiments.
How good is a model? Profit Curves, ROC curves, and the expected value formalism.
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https://arxiv.org/abs/2306.08302
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Bob Boule
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- 3. 10 Anglocentrique اﻹﻧﺟﻠﯾزﯾﺔ اﻟﻠﻐﺔ ﻓﻲ اﻟﻧص ﻋن اﻟﺗﻔﻛﯾر 思考所有文字应是英文 אנגלית רק ומדברים כותבים לא כולם למה תוהים
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- 6. 7 Subjective, anecdotal rather than objective, empirical evaluation
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