Process Optimization using Machine Learning in Practice
Next Date
Mon/Tue, 14-15/12/2026
Participants
3 - 12
Duration
2 Days
Course Form
online, classroom in Basel or in-house ℹ
He has over 25 years of experience in the application of statistical methods in the process industry. He has coached many training sessions on various topics, including Statistical Quality and Process Control. His wide practical knowledge allows him to make even technical concepts easily understandable by participants.
Dr. Solot holds an M.Sc. and a Ph.D. in Applied Mathematics, both from the EPFL (Swiss Federal Institute of Technology, Lausanne).
Course Description
Are you interested in extracting more information from your process data? This course will teach you how to get an overview of such data, select suitable analysis parameters, and prepare the data for its analysis. It is mainly aimed at people involved in quality and production who already have some basic statistical knowledge.
What Are You Going to Learn?
This course provides a hands-on instruction how to get from the raw process data to relevant information. A strong emphasis is put on the applicability of the proposed methods along a procedure going from initial data review to advanced data analysis. Machine Learning methods for root cause analysis and process optimization are introduced, with a focus on intuitive classification and regression trees (CART) to understand the impact of parameters like temperature or duration. All methods are demonstrated using modern analysis tools and practised through PC-based exercises, enabling you to independently analyze your own data.
Who Should Attend?
Everyone who wants to draw more information from available data
Elementary statistical knowledge (as provided in “Visualization of Lab Data with Excel”) is assumed.
Course Fees
- CHF 795.- per day + 8.1% VAT (incl. course documents and individual participant coaching during the practical exercises) for online courses
- CHF 865.- per day + 8.1% VAT (incl. course documents, individual participant coaching during the practical exercises, break refreshments and lunch) for classroom courses.
Which Topics Are Covered?
Obtaining an Overview
Histogram and box plot
Scatter plot, correlogram and density plots
Data Preparation
Outlier treatment
Data reduction
Methods of Machine Learning
Linear models
Classification and Regression Trees (CART)
Result visualization and interpretation
Any Questions? Send Us a Message.
Since we are a small company, we can respond quickly to your questions. Just contact us either by telephone +41 61 686 98 77 or using the Contact form.