This repository includes the course work for Machine Learning on Coursera by Andrew Ng.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. (source: https://www.coursera.org/learn/machine-learning)
The execution of .m files for exercises requires the installation of GNU Octave.
All the exercises are included in the folders from machine-learning-ex1 to machine-learning-ex8. Lecture slides are included in the lectures folder.