AI Fundamentals
Build a strong foundation in artificial intelligence and understand the concepts shaping intelligent systems.
This series explains what artificial intelligence is, how systems learn from data, how neural networks work, and how to judge whether a problem is a good fit for AI.
No mathematics or programming background is assumed. Definitions follow standard textbooks and course material, listed as references under each topic.
What You’ll Learn
Artificial intelligence is the field concerned with building systems that perceive their environment and take actions to achieve goals, a definition used in Russell and Norvig’s standard textbook. In practice, most modern AI is machine learning: rather than following rules written by hand, a system learns its behaviour from examples.
Traditional software is told exactly what to do. A learning system is shown many examples and works out the pattern itself. That is why it copes with messy real-world input, and also why it is never perfectly predictable.
Email spam filters are one of the oldest large-scale uses of machine learning. They learn from millions of messages that users mark as spam or not spam and apply those patterns to new mail.