A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.

This is an open access book. It is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

In geospatial analysis, navigating the complexities of data interpretation and analysis presents a formidable challenge. Traditional methods often need to efficiently handle vast volumes of geospatial data while providing insightful and actionable results. Scholars and practitioners grapple with manual or rule-based approaches, hindering progress in understanding and addressing pressing issues such as climate change, urbanization, and resource management. Ethics, Machine Learning, and Python in Geospatial Analysis offers a solution to the challenges faced by leveraging the extensive library support and user-friendly interface of Python and machine learning. The book’s meticulously crafted chapters guide readers through the intricacies of Python programming and its application in geospatial analysis, from fundamental concepts to advanced techniques.

This proceeding comprises peer-reviewed papers of the 2021 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2021), held from 15-17 November 2021 in Jeju, South Korea. This book deals with various themes on computational fluid dynamics, wind tunnel testing, flow visualization, UAV design, flight simulation, satellite attitude control, aeroelasticity and control, combustion analysis, fuel injection, cooling systems, spacecraft propulsion and so forth. So, this book can be very helpful not only for the researchers of universities and academic institutes, but also for the industry engineers who are interested in the current and future advanced topics in aerospace technology.

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.

This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title is also available as Open Access on Cambridge Core.

The second edition of Instant Notes in Bioinformatics introduced the readers to the themes and terminology of bioinformatics. It is divided into three parts: the first being an introduction to bioinformatics in biology; the second covering the physical, mathematical, statistical and computational basis of bioinformatics, using biological examples wherever possible; the third describing applications, giving specific detail and including data standards. The applications covered are sequence analysis and annotation, transcriptomics, proteomics, metabolite study, supramolecular organization, systems biology and the integration of-omic data, physiology, image analysis, and text analysis.

This book constitutes the refereed proceedings of the 5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications, DependSys, held in Guangzhou, China, in November 2019. The volume presents 39 full papers, which were carefully reviewed and selected from 112 submissions. The papers are organized in topical sections on ​dependability and security fundamentals and technologies; dependable and secure systems; dependable and secure applications; dependability and security measures and assessments; explainable artificial inteligence for cyberspace.

The term "machine learning" refers to a variety of computer technologies that make use of previous data in order to either enhance performance or develop more accurate predictions. The term was coined by British computer scientist Stuart Russell. The collective term for these many modes of instruction is "deep learning." In the context of this situation, the term "experience" refers to the historical knowledge that has been amassed and is now accessible to the student. This knowledge is what is supposed to be referred to as "experience." The vast majority of the time, this information is stored in the form of electronic data that may be investigated when it is necessary to do so. This data may be collected in the form of digitized human-labeled training sets, or it could be received in the form of any other kind of information that is gained by coming into touch with the environment. When it comes to determining how accurate the predictions of a learner are, the things that count the most are the kind of the object that is being anticipated as well as the quantity of that item that is being forecasted. An example of a learning challenge would be to find a way to properly predict the topic of papers that have not been read by looking at a limited number of documents that have been selected at random and tagged with themes. This might be accomplished by looking at a small number of documents that have been categorized. In this scenario, the student is challenged with coming up with a solution to the issue of how to accurately identify the topic of articles that have not yet been read. If there are more persons involved in the sample, then the task should, in principle, be simpler to finish. However, the level of difficulty of the assignment also relies on the quality of the labels that were applied to the papers in the sample. This will make the work more or less challenging. Because of this, the task might either become much simpler or significantly more challenging. This is because some of the labels could not be completely correct, and it also is depending on the number of subjects that can be accessed. The process of machine learning calls for the development of prediction algorithms that are capable of producing outcomes that are both accurate and efficient.