Webinar: Key aspects of Machine Learning for Systems Engineers

Webinar Highlights:

As systems engineers, understanding the key aspects of machine learning (ML) is essential to harnessing its potential and integrating it into complex systems.

This webinar will provide an overview of the critical elements of ML, accompanied by relevant examples and technologies. Creating an AI application involves several key steps, each critical to the success of the project. Using a real-world example of a Smart Buildings for better utilizing renewable energy during the webinar, we go through this step, defining their purpose and outcome and have a look a the hype about physics informed machine learning.

1. Data Collection and Preprocessing

One of the foundational aspects of ML is data collection and preprocessing. The quality and quantity of data significantly impact the performance of ML models.

2. Feature Engineering

Feature engineering involves selecting, modifying, or creating new features from raw data to improve model performance.

3. Model Selection and Evaluation

Choosing the right ML model and evaluating its performance are crucial steps. Different models have varying strengths, and their suitability depends on the problem at hand. Since most SE-Applications result in physical systems we have a closer look on using machine learning to simplify models.

Key Aspects:

· Time Series and the need to remember

· Cross-validation techniques

· AutoEncoder

· Model costume loss functions

4. Model Deployment

Deploying an ML model into production involves integrating it with existing systems and ensuring it functions as expected in real-world scenarios.

5. Monitoring and Maintenance

After deployment, it is essential to monitor the ML model’s performance and maintain it to adapt to changing data and requirements.

In conclusion, understanding these key aspects of machine learning is critical for systems engineers to successfully integrate ML into their systems. By leveraging the appropriate technologies and methodologies, systems engineers can build robust, efficient, and scalable ML solutions that drive innovation and improve system performance.