Introduction to Machine Learning
Core ML concepts from linear regression to neural networks. Click a chapter to explore.
What is machine learning and when do we need it?
Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed (Arthur Samuel). A computer program is said to learn from experience E with respect to tasks T and performance measure P, if its performance at T, as measured by P, improves with E (Tom Mitchell). This chapter establishes the vocabulary and taxonomy you will use throughout the entire course.
Three complementary definitions anchor the field. Understanding the Task–Performance–Experience (T–P–E) framework lets you formally specify any ML problem before choosing an algorithm.
Learning is any process by which a system improves its performance from experience. This captures the core idea: the system gets better over time by seeing more data.
A program learns from experience E with respect to task T and performance P if performance at T, measured by P, improves with E. Always specify all three when formulating a new ML problem. Example — spam filter: T = email categorisation, P = classification accuracy, E = labelled email data.
ML is the field of study that gives computers the ability to learn without being explicitly programmed. The key distinction from traditional software: you provide data and desired outputs, and the computer infers the rules.
Q1.As model complexity increases, the bias-variance tradeoff predicts: