Machine Learning with PLS_Toolbox/Solo
October 9, 2023
Eigenvector Research, Inc. is pleased to offer Machine Learning with PLS_Toolbox/Solo, a live, in-person short course showing how to use the advanced machine learning methods in Eigenvector software. Emphasis will be on applications in spectroscopy, Near-Infrared (NIR) in particular.
Complete information about the course can be found by following the links below.
- Target Audience
- Course Description
- About the Instructor
- Course Fee–It’s Free!
- Registration and Deadlines
- Course Outline
Machine Learning with PLS_Toolbox/Solo is aimed at spectroscopists and other scientists who want to be able to use machine learning methods to develop their own linear and non-linear models for calibration/regression or sample classification. It is recommended that participants be familiar with Principal Components Analysis (PCA) and multivariate regression methods such as Partial Least Squares (PLS). Courses in these topics can be found on the EigenU Recorded Courses page.
The material for this in-person course is drawn from our popular Eigenvector University Series. The course addresses linear methods briefly but will focus on machine learning methods designed to deal with non-linear data. Emphasis will be on applying these techniques to spectroscopic, especially NIR data. Methods for instrument calibration, sample classification and exploratory data analysis will be covered.
The course will focus on how to use the machine learning methods in Eigenvector’s PLS_Toolbox and Solo software. In order to take advantage of these hands-on examples participants should equip their computers with current versions of Solo or PLS_Toolbox (and MATLAB) installed. Demo copies will work just fine. Users with Eigenvector accounts can download free demos. If you don’t have an account, start by creating one.
The course will be led by Eigenvector Technology Principal Bob Roginski. Bob has delivered over 100 chemometrics courses at scientific conferences, on-site for companies, on-line and at our popular Eigenvector University each year in Seattle.
This course is free, however you must register to attend.
To register, login to your Eigenvector account, or create an account, then select the class you would like to attend under the “Purchase” tab. In order to assure a spot in the course, please register by Thursday, October 5, 2023.
Schedule, Central European Time (CET)
11:30 – 12:00 Check-in
12:00 – 13:30 Instruction
13:30 – 13:45 Coffee Break (with complimentary refreshments)
13:45 – 15:00 Instruction
15:00 – 15:30 Wrap-up Discussion and Questions
Machine Learning with PLS_Toolbox/Solo will cover the follow topics:
- Classification and Regression Methods
- Linear and Non-linear Data
- Partial Least Squares (PLS) Regression
- Partial Least Squares Discriminant Analysis (PLS-DA)
- Hierarchical Modeling (HMB and HMAC)
- Locally Weighted Regression (LWR)
- Soft Independent Modeling of Class Analogy (SIMCA)
- Support Vector Machine Discriminant Analysis (SVMDA)
- Artificial Neural Networks Regression and Discriminant Analysis (ANN, ANNDA)
- Boosted Regression Trees Discriminant Analysis (XGBoostDA)
- Hyperspectral Image Classification (time permitting)
- Conclusion/ Choosing Which Method
- Data Compression for Visualization and Preprocessing
- Brief review of Principal Components Analysis (PCA)
- t-distributed Stochastic Neighbor Embedding (t-SNE)
- Uniform Manifold Approximation and Projection (UMAP)
- Conclusion/ Choosing Which Method
- Discussion and Questions