Machine Learning Applications In Software Engineering

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algo

Explore the technology that is changing our world!Imagine a machine that can learn from experience and teach itself new things - that's AI in action! Trace the development of intelligent machines fr

This collection of twenty-one articles on online education showcases current scholarship in developing online technologies such as Learning Management Systems (LMS) and course management software. The

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

Clay behaviour is affected by coupled mechanical and chemical processes occurring in them at various scales. The peculiar chemical and electro-chemical properties of clays are the source of many undes

"This book explores this relatively new approach in software development that can increase the level of abstraction of the development of tasks, bridging the gaps between various disciplines within so

Reinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several practical RL applications for business intelligence, plant control, and

This multi-volume handbook is the most up-to-date and comprehensive reference work in the field of fractional calculus and its numerous applications. This eighth volume collects authoritative chapters

Finite element method is a powerful analysis tool in engineering field. While how it reflected in the industry field is still very unfamiliar for many people. This book presents a review of finite ele

Driven by advances in computer technology, engineering analysis has developed rapidly and extensively in recent times; Visualization of Fields and Applications in Engineering presents the basic techni

Unlike any other text of its kind, Materials Selection and Applications in Mechanical Engineering contains complete and in-depth coverage on materials of use, their principles, processing and handlin