Webinar: Key aspects of Machine Learning for Systems Engineers
Speaker: Joern Schlingensiepen
In this webinar, we will cover the essential aspects of machine learning that every systems engineer should know. Our expert speaker will give you an idea about the main steps in a ML project pipeline:
1. Data Collection and Preprocessing: Learn about the foundational aspects of ML, including the importance of data quality and quantity in determining the performance of ML models.
2. Feature Engineering: Discover how to select, modify, or create new features from raw data to enhance model performance.
3. Model Selection and Evaluation: Understand the process of choosing the right ML model and evaluating its performance. We will also explore the use of machine learning to simplify models of physical systems.
4. Model Deployment: Gain insights into deploying ML models into production, integrating them with existing systems, and ensuring they function as expected in real-world scenarios.
5. Monitoring and Maintenance: Learn about the importance of monitoring and maintaining ML models to adapt to changing data and requirements.
Throughout the webinar, we will use a real-world example of Smart Buildings to illustrate how ML can be leveraged for better utilizing renewable energy.
Additionally, we will discuss the hype around physics-informed machine learning and its potential applications.
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