Machine learning is becoming increasingly important in today's world. Time and again, there are innovations that are only possible with methods from the field of artificial intelligence and machine learning. The fields of application are very diverse:
- Industry 4.0 / Smartfactory
- Logistics and supply chain management
- Predictive Maintenance
- Customer analysis
You too can benefit from the new possibilities of modeling data. The Bochum University of Applied Sciences offers webinars on machine learning as part of WeAI, as well as classroom training at your company. You will get an introduction to Python and learn about the relevant methods from decision trees to Convolutional Neural Networks.
Audience: our courses are aimed at interested engineers, scientists and computer scientists who want to understand how the methods work and how to apply them. Basic knowledge of programming and mathematics is assumed.
Courses offered
In this four and a half day online training, you will receive a balanced mix of theory and practice on machine learning methods. You will learn about classical methods and also get a deep insight into neural networks. To enable you to work along with the practical exercises in between, we will start with an introduction to Python with the most important libraries in order to apply them to the methods afterwards:
- Basics of programming with Python
- Data processing with NumPy and Pandas
- Visualization of data
- Decision trees & Random Forests
- k-Nearest-Neighbors and hyperparameter optimization
- Support Vector Machines
- Regression and classification with artificial neural networks
- Time series and Convolutional Neural Networks
- Imputer methods
- Dimensionality reduction
- Clustering methods
- Detailed program with key data as PDF
Duration: 4.5 days, 8 hours per day (incl. breaks)
Platform: BigBlueButton, you only need a browser
Number of participants: 4 – 6
Costs: 5000 € (gross).
Included in the price:
- Online training
- Course materials as PDFs + practical elements as code
- Certificate of attendance
In this four and a half day face-to-face training, you will receive a balanced mixture of theory and practice on machine learning methods. You will get to know classical methods and also gain a deep insight into neural networks. To enable you to participate in the practical exercises in between, there is first an introduction to Python with the most important libraries in order to apply them to the methods afterwards:
- Basics of programming with Python
- Data processing with NumPy and Pandas
- Visualization of data
- Decision trees & Random Forests
- k-Nearest-Neighbors and hyperparameter optimization
- Support Vector Machines
- Regression and classification with Artificial Neural Networks
- Time Series and Convolutional Neural Networks
- Imputer methods
- Dimensionality reduction
- Clustering methods
- Detailed program with key data as PDF
Duration: 4.5 days, 8 hours per day (incl. breaks)
Number of participants: up to 12
Costs: 8000 € (gross), plus travel expenses
You provide the meeting room. The participants need a computer or laptop.
Included in the price:
- Face-to-face training
- Course materials as PDFs + practical elements as code
- Certificate of attendance
Learning goals
Learning goals: Kick-start into the methods of machine learning. Through theory and practical application, participants get an overview of which method is suitable for which application and learn how to implement it. After the course, participants are directly able to write their first applications.
With this webinar or in-house seminar you get everything you need to get started: an overview of the most common methods and the understanding to apply them. Maybe you already have data and are wondering how to train a model from it? That's exactly what you'll work through in the course with various sample data sets for regression and classification problems in concise exercises. The instructor will provide assistance with programming and will always be available to answer questions. So that the code doesn't seem like a black box to you, the elaborately illustrated material will give you an understanding of how the algorithms and neural networks work. This will help you better decide which methods are well suited for your data.
Team
Lecturer
Christof Kaufmann, M. Eng.
Campus Velbert/Heiligenhaus
christof.kaufmann@hs-bochum.de
+49 2056 5848 16743
Scientific Direction
Prof. Dr. Jörg Frochte
Campus Velbert/Heiligenhaus
joerg.frochte@hs-bochum.de
+49 2056 5848 16711
