Over the last decade, there has been considerable progress in investigating methods of symbolic mathematics in many application areas of computer science and artifical intelligence, such as engineering design, solid and geometric modelling, robotics and motion planning, and machine vision. This research has produced few applications within engineering and robotics because of the combinatorial cost of symbolic techniques. Therefore, it is essential to investigate approaches for systematic integration of symbolic with numerical techniques which are efficient for handling the huge amount of data that arises in practical applications, while at the same time maintain a logically consistent solution framework. Symbolic and Numerical Computation for Artificial Intelligence gives an overview of applications in machine vision, robotics and engineering design where there is a need for integrating symbolic and numerical methods. It also illustrates the case for an integrated symbolic and numerical environment to support the needs of these applications. This book will be essential reading for researchers in applied mathematics, symbolic and algebraic manipulation, and applied artificial intell

The purpose of this catalogue is to promote interaction between members of the AI' community. It will do this by announcing the existence of Ai techniques and portable software. and acting as a pOinter into the literature. Thus the AI community wili have access to a common. extensional definition of the field. which will: promote a common terminology. discourage the reinvention of wheels. and act as a clearing house for ideas and software. The cataiogue is a reference work providing a quick guide to the AI tools available for different jobs. It is not intended to be a textbook like the Artificial Intelligence Handbook. It. intentionally. only provides a brief description of each tool. with no extended discussion of the historical origin of the tool or how it has been used in particular AI programs, The focus is on techniques abstracted from their historical origins. The original version of the catalogue. was hastily built in 1983 as part of the UK SERC-Dol. IKBS. Architecture Study [lKBS Architecture Study 831. it has now been adopted by the SERC Specially Promoted Programme in IKBS and is kept as an on line document undergoing constant revision and refinement and published as a paperback by Springer Verlag.

Geosciences particularly numerical weather predication, are demanding the highest levels of computer power available. The European Centre for Medium-Range Weather Forecasts, with its experience in using supercomputers in this field, organizes a workshop every other year bringing together manufacturers, computer scientists, researchers and operational users to share their experiences and to learn about the latest developments. This volume provides an excellent overview of the latest achievements and plans for the use of new parallel techniques in the fields of meteorology, climatology and oceanography.

Computational mechanics is a scientific discipline that marries physics, computers, and mathematics to emulate natural physical phenomena. It is a technology that allows scientists to study and predict the performance of various productsâ€"important for research and development in the industrialized world. This book describes current trends and future research directions in computational mechanics in areas where gaps exist in current knowledge and where major advances are crucial to continued technological developments in the United States.

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 chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. What You'll Learn Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its related ecosystem for numerical computing.

Machine learning is a relatively new branch of artificial intelligence. The field has undergone a significant period of growth in the 1990s, with many new areas of research and development being explored.