Machine Learning

Machine learning is a field of AI in which systems improve performance on a task by learning patterns from data rather than relying solely on fixed rules.

Instead of writing every rule, you show the system examples and it learns how to make predictions or decisions.

ML models estimate functions from data by optimizing an objective (loss) with algorithms such as gradient descent. Supervised, unsupervised, and reinforcement settings differ by the form of feedback available during learning.

Most production “AI” in enterprises still rests on ML for scoring, ranking, forecasting, and detection. Distinguishing ML from generative LLM products clarifies vendor comparisons.

• Fraud and risk scoring
• Demand and churn forecasting
• Personalization and ranking

• Improves with more relevant data
• Automates repetitive prediction tasks
• Supports measurable A/B evaluation

• Needs careful validation and monitoring
• Can encode dataset bias
• May underperform when the world drifts

• More parameters are not always better for every problem
• Accuracy alone can hide unfair error rates
• ML does not remove the need for domain expertise

How does Brel assign the Machine Learning tag?

When company materials describe learning from data for predictions or decisions without a narrower modality being primary.

• NIST AI RMF

• Hastie, Tibshirani & Friedman — The Elements of Statistical Learning