Neural Networks and Genetic Algorithms in Practice
Next Date
By arrangement
Participants
3 - 12
Duration
1 Day
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 Machine Learning. 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
Do you own large and complex data sets and suspect that they hide interesting information? How powerful would it be to use this data for reliable predictions? Or do you need to identify optimal settings for the input parameters of a system? This course will introduce you to the fundamental concepts of neural networks and genetic algorithms as powerful tools for modelling complex relationships and for global optimization. It is aimed at managers, scientists and engineers who need to address efficiently intricate data-driven challenges.
What Are You Going to Learn?
The course first addresses neural networks. You will acquire knowledge about their origin, their structure, and how they can learn from historical data to model complex patterns, especially where classical statistical methods reach their limits. A variety of applications in fields such as the chemical industry, robotics, finance, pattern recognition, and medicine, will be presented. In the second part of the course, the complementary topic of genetic algorithms is considered. These all-purpose robust optimization techniques indeed prove helpful to overcome the difficulty of the neural network learning process, as well as to optimize their structure. All methods are illustrated through practical examples and PC-based software demonstrations.
Who Should Attend?
Managers, scientists and engineers
A minimal knowledge of mathematics is recommended, but not necessary.
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?
Neural Networks
History
Feed-forward networks
Learning process with data (backpropagation)
Advantages and disadvantages of neural networks
Applications and software overview
Genetic Algorithms
Biological motivation
Basic terms: fitness, selection, recombination, mutation
Advantages and disadvantages
Application examples (incl. optimization of neural networks)
Neural Networks and Statistics
Differences
Similarities
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.