Machine Learning Chapter Notes

Chapter 1 : Introduction to Machine Learning

Traditional computer programming relies on explicit instruction. A human programmer analyzes a problem, constructs a precise algorithm, and codes logical rules into software. This paradigm works well for deterministic tasks like calculating interest or generating invoices. Howeve…

Chapter 2 : Introduction to Supervised Learning

Supervised learning is the most widely deployed branch of artificial intelligence in industrial applications. The core principle of supervised learning is simple: we provide a learning algorithm with historical examples containing both input features and known target answers. The…

Chapter 3 : Bayesian and Instance-based Learning

Machine learning algorithms process data under uncertainty, noise, and incomplete information. To build reliable systems, algorithms must quantify uncertainty and update predictions as new evidence arrives. Probability theory provides the mathematical language needed to handle un…

Chapter 4 : Introduction to un-supervised learning and dimensionality reduction

Machine learning algorithms fall into distinct paradigms based on how they process input data and update their internal parameters. Supervised learning algorithms rely on annotated datasets that pair input features with explicit target labels. This target guidance allows models t…

Chapter 5 : Measures for Performance Evaluation of ML algorithms

Building predictive models represents only the initial phase of the machine learning workflow. Once an algorithm processes input features and adjusts its internal parameters, developers must evaluate how accurately the model generalizes to unseen observations. A model may achieve…