
Computational Methods for Data Analysis by Yeliz Karaca
This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.
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Engineering Sustainability Goals
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Perovskite Solar Cells
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Biopharmaceutical Manufacturing
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Natural Poisons and Venoms
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Self-Replicating Intelligent Systems
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Artificial Intelligence in Biotechnology
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Biocatalysis and Biotransformations
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Computational Finance
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Set: Natural Poisons and Venoms 1+2+3+4+5
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Industrial Green Chemistry
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Composites Recycling
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AI Ethics and Governance
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Artificial Intelligence in Society
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Technological Innovation - An Introduction
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Unified Compliance
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The Dynamic Equity Framework Theory
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Open Mathematical Problems
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Deepfake and Image Forgery Detection
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Ionic Polymers
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4D Printing in Healthcare
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Mucilage and Gums
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Gamifying Classrooms
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Digital Trust
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Polymer Interfacial Characteristics
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Computer-Aided Intelligent Imaging
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Energy, Innovation and Environment
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Functional Foods and Gut Microbiome
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The Riemann Zeta Function
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Intermetallics
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Artificial Intelligence in Higher Education
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Bio-Based Resources: Rethinking Sustainable Supply
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Retrofitting
Yeliz Karaca, University of Massachusetts, Worcester MA, USA; Carlo Cattani, University of Tuscia, Viterbo, Italy.
| SKU | Unavailable |
| ISBN 13 | 9783110496352 |
| ISBN 10 | 3110496356 |
| Title | Computational Methods for Data Analysis |
| Author | Yeliz Karaca |
| Series | De Gruyter Stem |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | De Gruyter |
| Year published | 2018-12-17 |
| Number of pages | 395 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |































