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Machine Learning Classification Algorithms using MatLab [Size: 580 MB] ... This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox. We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines.

Table of Contents: - Segment 1: Instructor and Course Introduction - Segment 2: MATLAB Crash Course - Segment 3: Grabbing and Importing Dataset - Segment 4: K-Nearest Neighbor - Segment 5: Naive Bayes - Segment 6: Decision Trees - Segment 7: Discriminant Analysis - Segment 8: Support Vector Machines - Segment 9: Error Correcting Output Codes - Segment 10: Classification with Ensembles - Segment 11: Validation Methods - Segment 12: Evaluating Performance. This course is really good for a beginner. It will help you to start from the ground up and move on to more complicated areas. You receive knowledge from a Ph.D. in Computer science (machine learning) with over 10 years of teaching and research experience.

Chester I. Duncan Jr. ... 408 pages - Publisher: Springer;  2nd edition (1998) ... Language: English - ISBN-10: 146137474X - ISBN-13: 978-1461374749

Soils and Foundations for Architects and Engineers, Second Edition is a practical guide to the technology of soil mechanics and foundations, and the application of that technology to the design and construction process. This text provides an up-to-date overview of the classification of soils, the design of foundations, and the behavior of soils under load. Particular emphasis has been given to the subject of piles, piers, and caissons, and to the design and details of construction of basement and retaining walls. New to this edition: Expanded coverage of shear strength of soils, settlement analysis, and expansive soil. + Design requirements for prestressed tiebacks, tiedowns, and rock anchors. + Expansion of information on pile driving techniques including the use of the Engineering News Formula. + A table of British-metric conversions. + Many new solved problems and illustrations. In addition to the numerous new improvements, the author also includes: effects of high water tables on architectural and engineering considerations, design of shear keys used in the transfer of lateral earth pressure from a wall to the supporting element, various drainage alternatives to the structural treatment of adjacent footings, and much more. Soils and Foundations for Architects and Engineers, Second Edition can be used in advanced undergraduate and graduate level courses offered in architectural engineering and civil engineering, as well as be used as a reference book by practicing architects, insurance adjusters and attorneys who litigate or adjudicate claims involving soils and foundations.

A. Schofield ... 216 pages - Publisher: Thomas Telford Publishing; (January, 2005) ... Language: English - ISBN-10: 0727729829 - ISBN-13: 978-0727729828 ...

This book describes the developments leading to the Original Cam Clay model, focusing on fundamentals of the shearing of soil. The aim is to lay the groundwork of understanding that should form the basis of geotechnical design, guiding engineers towards the class of behaviour to be expected under different combinations of effective stress and water content. In this book there are a few equations, but simple ones; much greater challenge rests in the arguments put forward regarding soil behaviour and the intellectual effort needed to keep pace with the author.

Learn Neural Networks using Matlab Course [Size: 187 MB] ... MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language developed by MathWorks. Although MATLAB is intended primarily for numerical computing, but by optional toolboxes, using the MuPAD symbolic engine, has access to symbolic computing capabilities too. One of these toolboxes is Neural Network toolbox. This toolbox is free, open source software for simulating models of brain and central nervous system, based on MATLAB computational platform. In these courses you will learn the general principles of Neural Network Toolbox designed in Matlab and you will be able to use this Toolbox efficiently as well.

The list of contents is: - Introduction: in this chapter the Neural Network Toolbox is Defined and introduced. An overview of neural network application is provided and the neural network training process for pattern recognition, function fitting and clustering data in demonstrated. - Neuron models:  A description of the neuron model is provided, including simple neurons, transfer functions, and vector inputs and single and multiple layers neurons are explained. The format of input data structures is very effective in the simulation results of both static and dynamic networks. So this effect is discussed in this chapter too. And finally the incremental and batch training rule is explained. - Perceptron networks: In this chapter the perceptron architecture is shown and it is explained how to create a perceptron in Neural network toolbox. The perceptron learning rule and its training algorithm is discussed and finally the network/Data manager GUI is explained. - Linear filters: in this chapter linear networks and linear system design function is discussed. The tapped delay lines and linear filters are discussed and at the end of the chapter LMS algorithm and linear classification algorithm used for linear filters are explained. - Backpropagation networks: The architecture, simulation, and several high-performance backpropagation training algorithms of backpropagation networks are discussed in this chapter. - Conclusion: in this chapter the memory and speed of different backpropagation training algorithms are illustrated. And at the end of the chapter all these algorithms are compared to help you select the best training algorithm for your problem in hand. - Matlab Software Installation: You are required to install the Matlab Software on your machine, so you can start executing the codes, and examples we work during the course.

Amos Gilat, Vish Subramaniam ... 480 pages - Publisher: Wiley; 1st edition (April, 2007) ... Language: English - ISBN-10: 0471734403 - ISBN-13: 978-0471734406 ...

Following a unique approach, this innovative book integrates the learning of numerical methods with practicing computer programming and using software tools in applications. It covers the fundamentals while emphasizing the most essential methods throughout the pages. Readers are also given the opportunity to enhance their programming skills using MATLAB to implement algorithms. They'll discover how to use this tool to solve problems in science and engineering. A clear and concise guide to numerical methods and their application: Mastering numerical methods has never been easier than with Gilat/Subramaniam’s Numerical Methods For Engineers and Scientists: An Introduction with Applications Using MATLAB. Uniquely accessible and concise, this book takes an innovative approach that integrates the study of numerical methods with hands-on programming practice using the popular MATLAB environment to solve realistic problems in engineering and science. Ideal for both students and professionals who would like to become more adept at numerical methods, Numerical Methods For Engineers and Scientists familiarizes you with: The mathematical background and fundamentals of numerical methods + Solving  nonlinear equations + Solving a system of linear equations + Eigenvalues and Eigenvectors + Function approximation, curve fitting, and interpolation + Differentiation + Integration + First-order and higher-order ODEs + Initial and boundary value problems. Using MATLAB’s built-in functions as tools for solving problems, you will practice applying numerical methods for analysis of real-world problems. All the information is presented in manageable, step-by-step fashion, supported by a large number of annotated examples and end-of-chapter problems. Lucid, carefully structured, and flexibly designed to fulfill a wide range of academic and practical needs, this book will help you develop the skills in numerical methods that will serve you well as a practicing engineer.

Amos Gilat ... 416 pages - Publisher: Wiley; 5th edition (January, 2014) ... Language: English- ISBN-10: 1118629868 - ISBN-13: 978-1118629864 ...

More college students use Amos Gilat’s MATLAB: An Introduction with Applications than any other MATLAB textbook. This concise book is known for its just-in-time learning approach that gives students information when they need it. The new edition gradually presents the latest MATLAB functionality in detail. Equally effective as a freshmen-level text, self-study tool, or course reference, the book is generously illustrated through computer screen shots and step-by-step tutorials, with abundant and motivating applications to problems in mathematics, science, and engineering.