What is Machine Learning?
Machine Learning (ML) is a form of artificial intelligence (AI) in which systems are able to learn and improve themselves on the basis of data, without being explicitly programmed for every possible situation.
Machine Learning models discover patterns in data and use that knowledge to make predictions, take decisions or detect anomalies.
🧠 How does Machine Learning work?
- Collect data: input data (such as temperature measurements, images, text, etc.)
- Train the model: the algorithm searches for patterns in the data
- Test/validate the model: performance is evaluated against new, unseen data
- Deploy (inference): the model is used to make live predictions
🔍 Types of Machine Learning
| Type | Description | Examples |
|---|---|---|
| Supervised learning | Learning with labelled data (input + correct output known) | Quality classification, predicting consumption |
| Unsupervised learning | Learning without labelled output, finding structures itself | Cluster analysis, anomaly detection |
| Reinforcement learning | Learning through rewards and penalties for actions | Robot navigation, games, automated control |
🏭 Applications in industry and IT
- Predictive maintenance (predicting failures based on sensor data)
- Visual inspection with AI cameras (Vision, Deep Learning)
- Anomaly detection in Cybersecurity and SIEM
- Process optimisation (energy consumption, product quality)
- Natural Language Processing for log analysis or ticket classification
- Demand forecasting in supply chain or production planning
🧱 Common algorithms
- Decision Trees
- Random Forests
- Support Vector Machines (SVM)
- K-Means Clustering
- Neural Networks (for Deep Learning)
✅ Benefits of Machine Learning
- Self-learning: performance improves with more data
- Adapts to changing conditions
- Detects hidden patterns or anomalies
- Can automate or support processes
🚧 Considerations
- Requires sufficient good and representative data
- Models must be validated and kept up to date
- Explainability can be challenging (especially with Deep Learning)
- In OT environments, reliability and transparency are crucial
📌 In summary
Machine Learning enables systems to learn from data and recognise patterns, leading to smarter, faster and more automated decisions — with broad applications in industry, IT and Cybersecurity.