Fundamentals of AI and ML

The broad field of building machines that perform tasks normally requiring human intelligence (perception, reasoning, decision-making).

Traditional software follows explicitly coded rules; AI aims to mimic human-like intelligent behavior, often by learning from data.

A subset of AI where systems learn patterns from data to make predictions or decisions, instead of being explicitly programmed with rules.

ML is useful when the rules are too complex, unknown, or constantly changing to specify manually but patterns exist in data.

A subset of ML that uses multi-layer (deep) neural networks to learn hierarchical features, excelling at images, language, and audio.

On large, unstructured data like images, natural language, and audio where deep networks learn complex features automatically.