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- Linear Algebra Course - Eigenvector Research, Inc.

Us Search Site Search for Linear Algebra for Chemometricians Course Description It has been said that linear algebra is the language of chemometrics Linear Algebra for Chemometricians provides the necessary mathematical background required to understand what s going on under the hood of most chemometric methods and makes chemometric literature accessible This half day course introduces concepts that are required for a complete understanding of common techniques such as Principal

Original URL path: http://eigenvector.com/courses/EigenU_LinAlg.html (2016-04-27)

Open archived version from archive - Matlab Course - Eigenvector Research, Inc.

working with large quantities of data such as that found in most chemometric applications MATLAB for Chemometricians covers the basic MATLAB operations needed to use Eigenvector s chemometrics software e g PLS Toolbox Data import plotting and getting help are also covered in this half day course Students will also learn to write basic scripts and functions thus opening the door to programming custom routines for data analysis Prerequisites Linear

Original URL path: http://eigenvector.com/courses/EigenU_MATLAB.html (2016-04-27)

Open archived version from archive - Chemometrics I Course - Eigenvector Research, Inc.

perhaps the most important chemometric method Principal Components Analysis PCA can be used for exploratory data analysis pattern recognition data prescreening and is part of many other methods such as SIMCA sample classification It is also used for preprocessing data in a wide variety of applications This course covers the basics of PCA in depth concentrating on interpretation of PCA models The course includes hands on computer time for participants

Original URL path: http://eigenvector.com/courses/EigenU_Chemo1.html (2016-04-27)

Open archived version from archive - Chemometrics II Course - Eigenvector Research, Inc.

PLS regression is a very close second Chemometrics II Regression and PLS covers regression methods starting with Classical Least Squares CLS and Multiple Linear Regression MLR and culminates in Principal Components Regression PCR and PLS Regression Students will learn to safely apply the methods to create predictive models in a variety of applications The course includes hands on computer time for participants to work example problems using PLS Toolbox Prerequisites

Original URL path: http://eigenvector.com/courses/EigenU_Chemo2.html (2016-04-27)

Open archived version from archive - Example Course Outline - Eigenvector Research, Inc.

the command line 3 5 Functions and scripts 3 6 Demonstrations 4 Statistics used in chemometrics 4 1 Distributions t tests F tests 4 2 The Central Limit Theorem 4 3 Analysis of Variance ANOVA 4 4 Experimental design 4 5 MATLAB examples and homework problems 5 Principal Components Analysis PCA 5 1 Nomenclature and conventions 5 2 Data transformation Linearization 5 3 Data centering and scaling 5 4 The PCA decomposition 5 5 Examples Wine and Arch data sets 5 6 Scores and loadings plots 5 7 Q and T2 statistics 5 8 Outliers 5 9 Determination of number of factors to keep 5 10 Example Chemical process monitoring 5 11 MATLAB examples and homework problems 6 Building Predictive Models Regression 6 1 Nomenclature and Conventions 6 2 Classical Least Squares CLS 6 3 Inverse Least Squares ILS models 6 4 Multiple Linear Regression MLR 6 5 Ridge Regression RR 6 6 Principal Components Regression PCR 6 7 Determination of number of PCs Cross Validation 6 8 Partial Least Squares PLS 6 9 Outlier detection and model diagnostics 6 10 A unifying theme Continuum Regression CR 6 11 Summary 6 12 MATLAB examples and homework problems 7 Non Linear Modeling Methods 7 1 Fitting Polynomials to data 7 2 Artificial Neural Networks ANNs 7 3 Non linear versions of PCR and PLS 7 4 Locally Weighted Regression LWR 7 5 Hybrid models NN PLS 7 6 Genetic algorithms for structure selection 7 7 Comparison of methods a non linear modeling problem 7 8 Summary The importance of model structure 8 Dealing with laboratory or process instrument drift 8 1 Baselining 8 2 Use of derivatives 8 3 Instrument Standardization 8 4 MATLAB examples 9 Supervised Pattern Recognition Classification 9 1 Classes of classification methods 9 2 Linear Discriminant Analysis

Original URL path: http://eigenvector.com/courses/Courseout.html (2016-04-27)

Open archived version from archive - Managing Data and Models - Eigenvector Research, Inc.

functions objects and files This course aims to show how to organize program and customize PLS Toolbox We ll explore the architecture of PLS Toolbox and it s core DataSet and Model objects We will show how to control and customize plots as well as other PLS Toolbox interfaces Finally we ll look at how to create reports and publish results This course will be useful for users who want to partially or completely automate their data analysis workflow Users who want to improve their PLS Toolbox programming and customize interfaces will also get a lot out of this course Hands on exercises will be done using MATLAB and PLS Toolbox Prerequisites Linear Algebra for Chemometricians MATLAB for Chemometricians Chemometrics I PCA and Chemometrics II Regression and PLS or equivalent experience Course Outline 1 0 Introduction 1 1 Organization of PLS Toolbox 1 2 Background and History of Models and DataSet Objects DSOs 1 3 Introduction to Object Oriented Programming 2 0 Managing Data and Models 2 1 DataSet Object Properties and Methods Importing and Exporting Building up a DSO from Multiple Sources 2 2 Preprocessing Basic Usage Developing Custom Methods 2 3 Model Objects Properties and Methods Importing and

Original URL path: http://eigenvector.com/courses/EigenU_Data_and_Models.html (2016-04-27)

Open archived version from archive - Model Deployment with Solo_Predictor and Model_Exporter - Eigenvector Research, Inc.

on examples of how to use each approach and the situations where each is more appropriate Solo Predictor is a stand alone server which permits previously built Eigenvector models to be used to make predictions on new data supplied by a user We will describe Solo Predictor installation and configuration to suit the commonest usage needs and available client communication methods Its abilities will be detailed especially its versatile Solo Scripting language its web browser monitoring interface and troubleshooting issues Model Exporter is an add on feature to PLS Toolbox or Solo which enables the export or translation of models into text form based on an interpretable format for use outside of these products These exported models can be used on other systems and programming language environments such as C or Java by using our provided interpreters or a user supplied interpreter to make predictions on new data The course includes hands on computer time using MATLAB and PLS Toolbox for participants to understand better the differences between the various options Prerequisites Linear Algebra for Chemometricians MATLAB for Chemometricians Chemometrics I PCA and Chemometrics II Regression and PLS or equivalent experience Course Outline 1 0 Overview of model deployment approaches

Original URL path: http://eigenvector.com/courses/EigenU_Online_Models.html (2016-04-27)

Open archived version from archive - Non-linear Methods Course - Eigenvector Research, Inc.

linear methods have been developed however we will focus on a few that we have found quite useful The course begins with a discussion of linearizing transforms Augmenting with non linear transforms e g polynomials is discussed next Locally Weighted Regression LWR Artificial Neural Networks ANNs and Support Vector Machines SVMs are then considered with SVMS for both regression and classification considered The course includes hands on computer time for

Original URL path: http://eigenvector.com/courses/EigenU_NonLinear.html (2016-04-27)

Open archived version from archive