AI fundamentals
Definition
AI fundamentals cover the core ideas behind artificial intelligence: what we mean by learning, representation, and generalization. This includes supervised and unsupervised learning, optimization, and the relationship between data, models, and objectives.
These ideas underpin both classical machine learning and deep learning. Understanding them helps you choose the right paradigm, interpret results, and reason about limits (e.g. data requirements, bias, robustness).
How it works
In practice, data is collected or labeled; a model (e.g. a function or network) is chosen; and an objective (loss or reward) is optimized so the model fits the data. The result is a prediction (or action) on new inputs. The pipeline relies on mathematical foundations — probability, linear algebra, optimization — and evaluation on held-out data to ensure generalization rather than memorization.
Use cases
Core ML ideas apply wherever you have data and a well-defined prediction or optimization goal.
- Building classifiers (e.g. spam detection, sentiment analysis) from labeled data
- Learning representations for recommendation systems or search
- Framing decision-making as prediction or optimization (e.g. forecasting, control)
External documentation
- Google ML crash course — Introduction to ML concepts
- MIT 6.S191 – Introduction to Deep Learning — Lectures and materials