the Nest of Students

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.

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.

Statistician v2.00.01.79 [Size: 4.32 MB] ... Statistician is a comprehensive yet simple to use add-in for Microsoft Excel 2007 or later. The software performs a high quality statistical analysis based on a series of easy-to-use forms, activated via the Excel tab. Statistician works in a unique way when compared to other Excel based statistical analysis software. Most importantly, it allows the user to store a data set and perform multiple analyzes on it, a method used by all high end statistical software, but lacking in many other Excel add-ins that require the user to re-select data over and over again when they return to the spreadsheet. Many customers, having used alternative statistical data analysis in the past, are amazed by not only how powerful and reliable Statistician is, but how fast and easy it is to use.

Features: – conduct statistical analyzes different – a set of standard tests – tests of variance – test for parametric – Test normalization – the Mac is to sort and grade data – a logistic regression model and Probiotic regression model – Sampling – Optimization – Provide a summary of statistical results. Calculations: ANOVA - Multiple Regression Analysis - Variance Tests - Normality Tests - Non Parametric Tests - Chi Square Tests - Binary Models - Count Model - Multivariate Analysis - Time Series Analysis.

George E. P. Box,‎ George C. Tiao ... 588 pages - Publisher: Addison-Wesley Publishing; 1st edition (June, 1973) ... Language: English - ISBN-10: 0201006227 - ISBN-13: 978-0201006223 ... Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a prior. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly non-informative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.

Roger Fletcher ... 456 pages - Publisher: Wiley; 2nd edition (May, 2000) ... Language: English - ISBN-10: 0471494631 - ISBN-13: 978-0471494638 ...

Fully describes optimization methods that are currently most valuable in solving real-life problems. Since optimization has applications in almost every branch of science and technology, the text emphasizes their practical aspects in conjunction with the heuristics useful in making them perform more reliably and efficiently. To this end, it presents comparative numerical studies to give readers a feel for possibile applications and to illustrate the problems in assessing evidence. Also provides theoretical background which provides insights into how methods are derived. This edition offers revised coverage of basic theory and standard techniques, with updated discussions of line search methods, Newton and quasi-Newton methods, and conjugate direction methods, as well as a comprehensive treatment of restricted step or trust region methods not commonly found in the literature. Also includes recent developments in hybrid methods for nonlinear least squares; an extended discussion of linear programming, with new methods for stable updating of LU factors; and a completely new section on network programming. Chapters include computer subroutines, worked examples, and study questions. * Introduction * Structure of Methods * Newton-like Methods * Conjugate Direction Methods * Restricted Step Methods * Sums of Squares and Nonlinear Equations * Introduction * Linear Programming * The Theory of Constrained Optimization * Quadratic Programming * General Linearly Constrained Optimization * Nonlinear Programming * Other Optimization Problems.

Brian C. Cronk ... 136 pages - Publisher: Routledge; 5th edition (January, 2008) ... Language: English - ISBN-10: 1884585795 - ISBN-13: 978-1884585791 ...

Designed for use by novice computer users, this text begins with the basics, such as starting SPSS, defining variables, and entering and saving data. • All major statistical techniques covered in beginning statistics classes are included: descriptive statistics * graphing data * prediction and association * parametric inferential statistics * nonparametric inferential statistics * statistics for test construction. • Each section starts with a brief description of the statistic that is covered and important underlying assumptions, which help students select appropriate statistics. • Each section describes how to interpret results and express them in a research report after the data are analyzed. For example, students are shown how to phrase the results of a significant and an insignificant t test. • More than 200 screenshots (including sample output) throughout the book show students exactly what to expect as they follow along using SPSS. • A glossary of statistical terms is included, which makes a handy reference for students who need to review the meanings of basic statistical terms. • Practice exercises throughout the book give students stimulus material to use as they practice to achieve mastery of the program. • Thoroughly field-tested; your students are certain to appreciate this book.

Narasimha Karumanchi ... 434 pages - Publisher: CareerMonk Plublications; 5th edition (August, 2016) ... Language: English - ISBN-10: 819324527X - ISBN-13: 978-8193245279 ...

A handy guide of sorts for any computer science professional, Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles is a solution bank for various complex problems related to data structures and algorithms. It can be used as a reference manual by those readers in the computer science industry. The book has around 21 chapters and covers Recursion and Backtracking, Linked Lists, Stacks, Queues,Trees, Priority Queue and Heaps, Disjoint Sets ADT, Graph Algorithms, Sorting, Searching, Selection Algorithms [Medians], Symbol Tables, Hashing, String Algorithms, Algorithms Design Techniques, Greedy Algorithms, Divide and Conquer Algorithms, Dynamic Programming, Complexity Classes, and other Miscellaneous Concepts. Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles by Narasimha Karumanchi was published in March, and it is coded in C/C++ language. This book serves as guide to prepare for interviews, exams, and campus work. It is also available in Java. In short, this book offers solutions to various complex data structures and algorithmic problems.

Nataraj Venkataramanan, Ashwin Shriram ... 232 pages - Publisher: Chapman and Hall/CRC; (August, 2016) ... Language: English - ISBN-10: 1498721044 - ISBN-13: 978-1498721042

F. M. Dekking, C. Kraaikamp ... 488 pages - Publisher: Springer; (February, 2007) ... Language: English - ISBN-10: 1852338962 - ISBN-13: 978-1852338961 ...

Probability and Statistics are studied by most science students, usually as a second- or third-year course. Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work. The strength of this book is that it readdresses these shortcomings; by using examples, often from real-life and using real data, the authors can show how the fundamentals of probabilistic and statistical theories arise intuitively. It provides a tried and tested, self-contained course, that can also be used for self-study. A Modern Introduction to Probability and Statistics has numerous quick exercises to give direct feedback to the students. In addition the book contains over 350 exercises, half of which have answers, of which half have full solutions. A website at www.springeronline.com/1-85233-896-2 gives access to the data files used in the text, and, for instructors, the remaining solutions. The only pre-requisite for the book is a first course in calculus; the text covers standard statistics and probability material, and develops beyond traditional parametric models to the Poisson process, and on to useful modern methods such as the bootstrap. This will be a key text for undergraduates in Computer Science, Physics, Mathematics, Chemistry, Biology and Business Studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects.

Rafael A. Irizarry, Michael I. Love ... 376 pages - Publisher: Chapman and Hall/CRC; 1st edition (August, 2016) ... Language: English - ISBN-10: 1498775675 - ISBN-13: 978-1498775670 ...

This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.

John J. Kinney ... 480 pages - Publisher: Wiley; 2nd edition (December, 2014) ... Language: English - ISBN-10: 1118947088 - ISBN-13: 978-1118947081.

Learn how to use Tableau to see and understand your business's data better. Tableau is a key player in the business intelligence field. These tutorials will help you use this program to analyze and visualize your organization's data.

Mike McGrath ... 192 pages - Publisher: In Easy Steps Limited; (March, 2018) ... Language: English - ISBN-10: 1840787953 - ISBN-13: 978-1840787955 ...

The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends. R for Data Analysis in easy steps has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. R for Data Analysis in easy steps begins by explaining core programming principles of the R programming language, which stores data in “vectors” from which simple graphs can be plotted. Next, the book describes how to create “matrices” to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create “data frames” from imported data sets, and how to employ the “Grammar of Graphics” to produce advanced visualizations that can best illustrate useful insights from your data. R for Data Analysis in easy steps contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding. By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so it's ideal for the newcomer to computer programming.

Géza Schay ... 385 pages - Publisher: Birkhäuser; 2nd edition (June, 2016) ... Language: English - ISBN-10: 3319306189 - ISBN-13: 978-3319306186 ...

Now in its second edition, this textbook serves as an introduction to probability and statistics for non-mathematics majors who do not need the exhaustive detail and mathematical depth provided in more comprehensive treatments of the subject. The presentation covers the mathematical laws of random phenomena, including discrete and continuous random variables, expectation and variance, and common probability distributions such as the binomial, Poisson, and normal distributions. More classical examples such as Montmort's problem, the ballot problem, and Bertrand’s paradox are now included, along with applications such as the Maxwell-Boltzmann and Bose-Einstein distributions in physics. Key features in new edition: * 35 new exercises. * Expanded section on the algebra of sets. * Expanded chapters on probabilities to include more classical examples. * New section on regression. * Online instructors' manual containing solutions to all exercises. Advanced undergraduate and graduate students in computer science, engineering, and other natural and social sciences with only a basic background in calculus will benefit from this introductory text balancing theory with applications.

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. (To be more precise, neural networks extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.)

Gayle Cain ... 229 pages - Publisher: Nova Science Pub.; (January, 2017) ... Language: English - ISBN-10: 1634859642 - ISBN-13: 978-1634859646 ...

This current book provides new research on artificial neural networks (ANNs). Topics discussed include the application of ANNs in chemistry and chemical engineering fields; the application of ANNs in the prediction of biodiesel fuel properties from fatty acid constituents; the use of ANNs for solar radiation estimation; the use of in silico methods to design and evaluate skin UV filters; a practical model based on the multilayer perceptron neural network (MLP) approach to predict the milling tool flank wear in a regular cut, as well as entry cut and exit cut, of a milling tool; parameter extraction of small-signal and noise models of microwave transistors based on ANNs; and the application of ANNs to deep-learning and predictive analysis in semantic TCM telemedicine systems.

Rajendra Akerkar, Priti Srinivas Sajja ... 272 pages - Publisher: Springer; 1st edition (October, 2016) ... Language: English - ISBN-10: 3319292056 - ISBN-13: 978-3319292052 ...

This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions. The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for real‐world applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.

Paul Kinnear,‎ Colin Gray ... 464 pages - Publisher: Psychology Press; (July, 2004) ... Language: English - ISBN-10: 1841695246 - ISBN-13: 978-1841695242 ...

SPSS 12 Made Simple provides a step-by-step coverage of every aspect of data analysis with SPSS from data entry to interpretation of the output. As well as advice on data entry and checking, there is guidance on the best ways of describing a data set and the choice of an appropriate statistical technique. Finally, the output is fully explained, with reference to fully annotated SPSS output. Extensive illustrations show exactly what is on the screen at every stage of the process, helping the reader to avoid common pitfalls and check their progress along the way. Most chapters end with practical exercises to illustrate the main points raised and allow the reader to test their understanding; but there is a final general revision section with further exercises on a range of topics. SPSS 12 Made Simple: * Covers a wide range of statistical tests including t-tests, ANOVA, correlation, regression, multi-way frequency analysis, discriminant analysis, logistic regression and factor analysis. * Shows you how to get as much out of your data as possible. * Gives advice (with appropriate cautions and caveats) on choosing a statistical test. * Makes extensive use of annotated screen snapshots of SPSS output, windows and dialog boxes. * Includes both chapter-specific and general exercises.

Gerard Dreyfus ... 498 pages - Publisher: Springer; Reprint of 1st edition (November, 2010) ... Language: English - ISBN-10: 3642061877 - ISBN-13: 978-3642061875 ...

Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.

H. Mendel ... 218 pages - Publisher: CreateSpace Independent Publ.; (October, 2016) ... Language: English - ISBN-10: 1539439321 - ISBN-13: 978-1539439325 ...

This book initiates the reader into the techniques of data analysis with MATLAB. The major contents are as follows: Data Processing Importing and Exporting Data Plotting Data Missing Data Representing Missing Data Values Working with NaNs Interpolating Missing Data Inconsistent Data Filtering Data Detrending Data Differencing Data Interactive Data Exploration Marking Up Graphs with Data Brushing Making Graphs Responsive with Data Linking Interacting with Graphed Data Data Brushing with the Variables Editor Using Data Tips to Explore Graphs Descriptive Statistics Functions for Calculating Descriptive Statistics Regression Analysis Linear Correlation Linear Regression Fitting Data with Curve Fitting Toolbox Functions Interactive Fitting Programmatic Fitting MATLAB Functions for Polynomial Models Linear Model with Nonpolynomial Terms Multiple Regression Time Series Analysis Time Series Objects Time Series Data Sample Time Series Constructor Time Series Tools Importing and Exporting Data Plotting Time Series Selecting Data for Analysis Editing Data, Time, Attributes, and Events Processing and Manipulating Time Series