Introduction to Machine Learning for I4.0
Online Course: Introduction to Machine Learning for I4.0
8 - 9 May 2023, 10:00 - 16:00 CEST
Training for engineers and other professionals from all domains who would like to make the most out of their data
Organised by EIT Manufacturing CLC East in cooperation with EuroCC Austria and VSC Research Center (TU Wien)
Price: € 240,00 (including VAT)
Registration via eitmanufacturing-east.eu
This online course takes the participant on a journey to the fundamentals of supervised machine learning (ML). Apart from providing an overview, this course also gets hands-on with different ML methods such as Support-Vector Machines, Decision Trees, Random Forests and Ensemble Learning for regression and classification problems. It conveys ways to find the best suitable method and teaches participants how to fine-tune their method of choice.
This training is ideal for:
- Marketing professionals with programming experience
- Professionals in QM with programming experience
- Professionals in machine maintenance with programming experience
Participants will learn how to:
- Choose the best-suited ML method for a given problem
- Train an ML algorithm
- Evaluate the algorithms performance
- Fine-tune hyperparameters
Overview Machine Learning
Participants learn what ML is and which different forms there are and what the typical use cases are. They also learn about the scope of the ML journey they are about to begin.
- Performance metrics
Participants get to know how the performance of an ML algorithm can be measured and what needs to be taken into account.
- Regression methods
In this section, the first ML methods are introduced, both for regression as well as classification problems. Participants will set benchmarks against which all other algorithms will be measured.
- Support vector machines
Here, participants will get to know a powerful and widely applicable ML method.
- Decision trees
This is the third type of ML method that participants will learn about.
- Random forests
A single decision tree is often not sufficient. Therefore, participants will learn how to „combine“ several trees into a random forest. This requires considerable compute power.
- Model evaluation & hyperparameter tuning
Participants learn how to decide which model performs the best and how they can improve the best-performing model even further.
- Ensemble learning
With even more compute power, different ML methods can be combined to improve performance even further.
In this final topic, participants will get a glimpse of what can be done with deep learning – the next step in the ML journey.
The lectures will be held online from 10:00 – 16:00 CEST over the course of two days. The participation links will be provided after the purchase and before the course.
- The participants are expected to have at least basic programming skills in Python.
- The programming language of choice in this course is Python with libraries such as NumPy, Pandas, Scikit-Learn and Matplotlib.
- Participants will use their own laptops or workstations to do the hands-on exercises. The content is delivered with Jupyter notebooks on Google Colab, so participants should have a Google account in order to be able to participate fully.
Simeon Harrison (EuroCC Austria and VSC Research Center, TU Wien)
Full price for the course with course documentation: € 240,00 (including VAT)
Upon completion of the online course, participants will receive a certificate of attendance.
Register via EIT Manufacturing CLC East
Monika Primozic (Education Developer EIT Manufacturing CLC East)
Simeon Harrison (Trainer and Coordinator Training for Industries, EuroCC Austria and VSC)