Artificial intelligence for drug development, precision medicine, and healthcare (2020) Pdf Free Download

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There are many publications on artificial intelligence (AI) under various names: data mining, text mining, machine-learning (ML), pattern recognition, statistical learning, deep learning, bioinformatics, computational biology, computational linguistics, natural language processing, robotics. Most book publications are for those who don’t have any statistical background at all. This is because the significant achievements in recent years, especially in deep learning for image and language processing and voice recognition, have nothing to do with conventional statistics at all, even though they are heavily dependent on big data. Some AI books are statistically oriented under the names of statistical learning, pattern-recognition, and machine learning. Because traditional statistics and AI both deal with data and an increasing number of statisticians or data scientists have become involved in AI research, the meaning and scope of AI are constantly evolving. Sometimes the differences are unintelligible, but AI is clearly not equal to statistics. Here are some subtle differences: (1) AI ML emphasizes learning and prediction, classical statistics focuses on type-I error rate, (2) statistics and AI both deal with uncertainty, but the former focuses on the mathematical approach and probability distributions, while the latter is mainly algorithm-based. (3) ML takes its main aim at real world experiences, while classical statistics often performs under an ideal assumed probability distribution.

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