Eigenvector University Europe is in Rome, ITALY October 14-17, 2024 Complete Info Here!‎

Machine Learning Summer School

August 6, 2024 - August 8, 2024


Eigenvector Research, Inc. is pleased to offer Machine Learning Summer School, a live webinar-based short course covering advanced machine learning methods with applications in chemometrics and chemical data science.

Complete information about the course can be found by following the links below.

Target Audience

Machine Learning Summer School is aimed at engineers, chemists and other scientists who want to be able to use pattern recognition methods to analyze complex data and develop their own non-linear data models for calibration/regression or sample classification.

The course serves individuals with a need for exploratory data analysis, development of predictive models such as analytical instrument calibrations, soft sensor models, sample classification, or analysis of designed data sets. 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.

Course Description

This course will be delivered via webinar in three segments of three and a half hours each. The course material is drawn from our popular Eigenvector University series. The course will focus on machine learning methods designed to deal with non-linear data. Emphasis will be on applying these techniques in the chemical process and laboratory environment for pattern recognition, instrument calibration, sample classification and exploratory data analysis.

The course will include many follow-along examples. In order to take advantage of these, 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. If you have not used our software before, we recommend that you download it and try it out before the course. We have many recorded webinars which will help you get started and show how to use many popular chemometric and machine learning methods.

About the Instructors

The course will be led by Principal Consultant and Data Scientist Manuel A. Palacios along with Technology Principal Bob Roginski. They will be assisted by Eigenvector President Barry M. Wise. Eigenvector Research has delivered hundreds of chemometrics courses at scientific conferences, on-site for companies, on-line and at our popular Eigenvector University each year in Seattle.

Course Fee

Prices include instruction, course materials (provided in advance in .pdf format), access to the recorded version of the course, and a certificate of completion.

Prices shown are shown below. Payment must be received by 5pm PDT, Friday, August 2, 2024.

Regular
Academic
Machine Learning Summer School
$575
$165

Note: Payment must be received by 5pm PDT, Friday, August 2, 2024. Credit card orders are strongly encouraged. Acceptable forms of payment include MasterCard, VISA, American Express, and checks drawn on a US bank. Wire transfers can also be arranged.

Academic discount: University students and faculty are eligible for the academic rate. Verification of University affiliation is required by providing valid university mailing and e-mail address. Note that we define academic as “degree granting institution.”

How to Register, Deadlines and Cancellations

To register, login to your Eigenvector account, or create an account, then select “Machine Learning Summer School (online)” under the “Purchase” tab. You can pay directly with your MasterCard, VISA or American Express using our secure credit card processing. You may ask to be invoiced, however, Payments must be received by 5pm PDT, Friday, August 2, 2024.

Complete refunds will be made for cancellations prior to July 26, 2024. No refunds will be made for cancellations after that date, however, substitutions are gladly accepted.

Schedule

Daily Schedule, Pacific Daylight Time (PDT)
06:45 – 07:00 Webex available for login
07:00 – 08:00 Instruction
08:00 – 08:10 Coffee Break
08:10 – 09:10 Instruction
09:10 – 09:20 Coffee Break
09:20 – 10:20 Instruction
10:20 – 10:30 Wrap-up and questions

Course Outlines

Machine Learning Summer School will cover the following topics:

  • Day 1:  Introduction to Machine Learning in Chemometrics and Unsupervised Learning
    • Introduction to Machine Learning:  Definitions and Nomenclature
    • Unsupervised Learning Methods
      • Clustering Analysis
      • Principal Components Analysis (PCA)
      • t-Distributed Stochastic Neighbor Embedding (t-SNE)
      • Uniform Manifold Approximation and Projection (UMAP)
  • Day 2:  Supervised Learning in Chemometrics
    • Linear Algorithms for Regression
      • Multiple Linear Regression (MLR)
        • Regularized Linear Regression:  Ridge, Lasso, Elastic Nets
      • Principal Components Regression (PCR)
      • Partial Least Squares (PLS)
    • Linear Algorithms for Classification
      • Logistic Regression
      • Linear Discriminant Analysis (LDA)
      • Partial Least Squares Discriminant Analysis (PLS-DA)
    • Non-Linear Methods
      • Locally Weighted Regression (LWR)
      • Support Vector Machines (SVM)
      • Support Vector Machines for Classification (SVM-C)
  • Day 3:  Advanced Supervised Learning Methods
    • Brief Overview of XGBoost
    • Artificial Neural Networks (ANNs)
      • Neurons, Layers, activation functions
      • Forward propagation, backpropagation, gradient descent
      • Implementation using ANN/BPN
    • Deep Learning ANNs
      • Difference between deep learning and traditional ML
      • Implementation using TensorFlow and scikit-learn
    • Model Interpretation and Robustness Testing for Non-Linear Models
      • Variable Importance:  SHAP (Shapley Additive Explanations)
      • Robustness Tests
    • Common Mistakes in Chemometrics
    • Summary and Recap

Schedule for the Week
Tuesday —  Introduction to Machine Learning in Chemometrics and Unsupervised Learning
Wednesday — Supervised Learning
Thursday — Supervised Learning (cont.) and Interpretatbility; Summary and Recap