• Title Essential Engineering Mathematics
• Author(s) Michael Batty
• Publisher: BookBoon
• Paperback N/A
• eBook PDF (149 pages)
• Language: English
• ISBN-10: N/A
• ISBN-13: 978-87-7681-735-0
• Share This:  

Book Description

This textbook covers topics such as functions, single variable calculus, multivariate calculus, differential equations and complex functions. The necessary linear algebra for multivariate calculus is also outlined. More advanced topics which have been omitted, but which you will certainly come across, are partial differential equations, Fourier transforms and Laplace transforms.

It is by no means a comprehensive guide to all the mathematics an engineer might encounter during the course of his or her degree. The aim is more to highlight and explain some areas commonly found difficult, such as calculus, and to ease the transition from school level to university level mathematics, where sometimes the subject matter is similar, but the emphasis is usually different.

• N/A

• Amazon

• Elementry, High School, and Engineering Mathematics
• Applied Mathematics
• Calculus and Mathematical Analysis
• Algebra, Abstract Algebra, and Linear Algebra, etc.

• Essential Engineering Mathematics (Michael Batty)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF

• Essential Mathematics for Engineering Technicians (OP-TEC)

  This book explains mathematical jargon and develops the formulae used by engineers from first principles. The first chapter is a summary so the reader can quickly see where further study is needed. The book is in two parts, pure and applied.

• Mathematical Introduction to Deep Learning (Arnulf Jentzen, et al)

  This book aims to provide an introduction to the topic of deep learning algorithms, coverss essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures and algorithms.

• Mathematics for Machine Learning (Marc P. Deisenroth, et al.)

  This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It provides a beautiful exposition of the mathematics underpinning modern machine learning.

• Mathematics for CS and Machine Learning (Jean Gallier, et al.)

  Covering everything you need to know about machine learning, now you can master the mathematics, computer science and statistics behind this field and develop your very own neural networks!

• Mathematical Methods in Quantum Mechanics (Gerald Teschl)

  This book is a brief, but self-contained, introduction to the mathematical methods of quantum mechanics, with a view towards applications to Schrodinger operators. Very clear and detailed way and supplements it by numerous exercises.

• Mathematical Foundations of Quantum Theory

  This is a collection of lecture notes, all shared a common interest in answering quantum issues. The goal is to give a mathematically clear and self-containing explanation of the main concepts of the modern language of quantum theory.

• Explaining Logarithms (Dan Umbarger)

  This book shows the evolution of logarithmic ideas over 350 years. The author do believes that a quick review of mathematics as it was practiced for hundreds of years would be helpful for many students in understanding logarithms as they are still used today.

• Advanced Problems in Mathematics: Preparing for University

  This book is intended to help candidates prepare for entrance examinations in mathematics and scientific subjects, including STEP (Sixth Term Examination Paper). is recommended as preparation for any undergraduate mathematics course.

• Basic Arithmetic Student Workbook (Donna Gaudet, et al)

  This workbook is designed to lead students through a basic understanding of numbers and arithmetic. It allows students to see the big picture of math and helps students recognize algebra as a natural extension of arithmetic.

• Technical Mathematics (Morgan Chase)

  This developmental-level mathematics textbook is intended for career-technical students. It provides a thorough review of pre calculus topics ranging from algebra, geometry, and trigonometry, with a strong emphasis on their applications in specific occupations.

• Math for Trades: Volume 1 (Chad Flinn, et al.)

  This volume represents the building blocks for math training. The goal of this volume is to get students prepared for the more advanced topics that they will encounter during their trades math education.

• Math for Trades: Volume 2 (Chad Flinn, et al.)

  This volume continues where the Volume 1 left off. Volume 2 increases the challenge with topics such as converting units and working with equations, perimeter, area, and volume. Once again the material is presented from a trades perspective.