Informatics and Machine Learning Discover a thorough exploration of
how to use computational, algorithmic, statistical, and informatics
methods to analyze digital data Informatics and Machine Learning:
From Martingales to Metaheuristics delivers an interdisciplinary
presentation on how analyze any data captured in digital form. The
book describes how readers can conduct analyses of text, general
sequential data, experimental observations over time, stock market
and econometric histories, or symbolic data, like genomes. It
contains large amounts of sample code to demonstrate the concepts
contained within and assist with various levels of project work.
The book offers a complete presentation of the mathematical
underpinnings of a wide variety of forms of data analysis and
provides extensive examples of programming implementations. It is
based on two decades worth of the distinguished author's teaching
and industry experience. A thorough introduction to probabilistic
reasoning and bioinformatics, including Python shell scripting to
obtain data counts, frequencies, probabilities, and anomalous
statistics, or use with Bayes' rule An exploration of information
entropy and statistical measures, including Shannon entropy,
relative entropy, maximum entropy (maxent), and mutual information
A practical discussion of ad hoc, ab initio, and bootstrap signal
acquisition methods, with examples from genome analytics and signal
analytics Perfect for undergraduate and graduate students in
machine learning and data analytics programs, Informatics and
Machine Learning: From Martingales to Metaheuristics will also earn
a place in the libraries of mathematicians, engineers, computer
scientists, and life scientists with an interest in those subjects.
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