Machine Learning - complete course notes. The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. The topics covered are shown below, although for a more detailed summary see lecture 19. The only content not covered here is the Octave/MATLAB programming. All diagrams are my own or are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. What are these notes? Originally written as a way for me personally to help solidify and document the concepts, these notes have grown into a reasonably complete block of reference material spanning the course in its entirety in just over 40 000 words and a lot of diagrams!
The notes were written in Evernote, and then exported to HTML automatically. How can you help!? If you notice errors or typos, inconsistencies or things that are unclear please tell me and I'll update them. Content. Introduction to Machine Learning. Draft of Incomplete Notes by Nils J. Nilsson nilsson@cs.stanford.edu Description (as of ): From this page you can download a draft of notes I used for a Stanford course on Machine Learning.
The notes survey many of the important topics in machine learning circa the late 1990s. There have been many important developments in machine learning since these notes were written. Download the notes: Introduction to Machine Learning (2.1 MB) Although this draft says that these notes were planned to be a textbook, they will remain just notes. Nils J. Nilsson@cs.stanford.edu Copyright © 2014 Nils J. Course | Machine Learning. CS 229: Machine Learning (Course handouts) Lecture notes 1 (ps) (pdf) Supervised Learning, Discriminative Algorithms Lecture notes 2 (ps) (pdf) Generative Algorithms Lecture notes 3 (ps) (pdf) Support Vector Machines Lecture notes 4 (ps) (pdf) Learning Theory Lecture notes 5 (ps) (pdf) Regularization and Model Selection Lecture notes 6 (ps) (pdf) Online Learning and the Perceptron Algorithm.
(optional reading) Lecture notes 7a (ps) (pdf) Unsupervised Learning, k-means clustering. Lecture notes 7b (ps) (pdf) Mixture of Gaussians Lecture notes 8 (ps) (pdf) The EM Algorithm Lecture notes 9 (ps) (pdf) Factor Analysis Lecture notes 10 (ps) (pdf) Principal Components Analysis Lecture notes 11 (ps) (pdf) Independent Components Analysis Lecture notes 12 (ps) (pdf) Reinforcement Learning and Control Supplemental notes 1 (pdf) Binary classification with +/-1 labels.
Supplemental notes 2 (pdf) Boosting algorithms and weak learning.