ctgk/prml

This repository contains Python code that implements the machine learning algorithms described in "Pattern Recognition and Machine Learning," a textbook written by Christopher Bishop. The book covers a wide range of mathematical and statistical techniques used to train computers to recognize patterns in data, and this project puts those techniques into runnable code so readers can see them in action. The code is organized as Jupyter notebooks, one per chapter of the book. There are 13 notebooks in total, covering topics like probability distributions, linear models, neural networks, kernel methods, graphical models, mixture models, and sampling methods. Each notebook corresponds directly to a chapter, so you can read a section of the book and then open the matching notebook to experiment with the actual Python implementation. To run the notebooks, you need Python 3 and a few standard scientific computing libraries: numpy and scipy for the mathematics, matplotlib for drawing charts, and sklearn if you want to load standard datasets. You can also run them for free in Amazon SageMaker Studio Lab, a cloud computing environment that requires only a free email registration, so you do not need a powerful personal computer to get started. The project is aimed at students and practitioners who are working through Bishop's textbook and want executable code alongside the theory. It is a companion to the book rather than a standalone learning resource and does not attempt to teach machine learning from scratch on its own.