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.
HTML5 & CSS3 Fundamentals: Development for Absolute Beginners | Channel 9 - Vimperator 10 minutes, 20 seconds 35 minutes, 14 seconds 29 minutes, 21 seconds 25 minutes, 25 seconds 48 minutes, 58 seconds 20 minutes, 18 seconds 6 minutes, 59 seconds 15 minutes, 30 seconds 13 minutes, 0 seconds 27 minutes, 27 seconds 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.
Stevey's Blog Rants: The Universal Design Pattern - Vimperator Note: Today's entry is a technical article: it isn't funny. At least not intentionally. Update, Oct 20th 2008: I've added an Updates section, where I'll try to track significant responses, at least for a week or so. Contents Introduction Today I thought I'd talk about a neat design pattern that doesn't seem to get much love: the Properties Pattern. People use this pattern all over the place, and I'll give you a nice set of real-life examples in a little bit. But even though this pattern is near-universal, people don't talk about it very often. I hope to offer a different and richer perspective here. Three Great Schools of Software Modeling Before I tell you anything else about the Properties Pattern, let's review some of the most popular techniques we programmers have for modeling problems. I should point out that none of these techniques is tied to "static typing" or "dynamic typing" per se. Class Modeling You know all about this one. Relational Modeling XML Modeling Other schools Eclipse Lisp
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
An Introduction to Programming in Go - Vimperator Machine Learning: Genetic Algorithms Part 1 (Javascript) | Burak Kanber's Blog - Vimperator On September 4, 2012 The Introduction to “Machine Learning in Javascript” post provides a nice introduction and context for this post and the rest of the series. I like starting my machine learning classes with genetic algorithms (which we’ll abbreviate “GA” sometimes). Genetic algorithms are probably the least practical of the ML algorithms I cover, but I love starting with them because they’re fascinating and they do a good job of introducing the “cost function” or “error function”, and the idea of local and global optima — concepts both important and common to most other ML algorithms. Genetic algorithms are inspired by nature and evolution, which is seriously cool to me. One phrase I used above is profoundly important: “general-purpose”. One application that comes to mind is bipedal robot walking. Let’s build a GA in Javascript. The Problem Build a genetic algorithm in Javascript that reproduces the text “Hello, World!”. Naturally, everything starts with “Hello, World!” The Chromosome
The Absolute Beginner's Guide to Sass What is Sass? Sass stands for Syntactically Awesome Stylesheets Sass, and was created by Hampton Catlin. It’s a way to simplify your CSS workflow, making development and maintenance tasks easier. For instance, have you ever had to do a find-and-replace in your stylesheet to change a particular HEX color for a particular indecisive client? Or had to open up the calculator app to figure out a column width in a multi-column design? Sass introduces new concepts such as variables, mixins, nestings and selector inheritance. Sass looks similar to CSS, but without the semi-colons and curly braces. Given the following CSS: You’d write the following in Sass: #skyscraper_ad display: block width: 120px height: 600px #leaderboard_ad display: block width: 728px height: 90px Sass uses two-space indentations to define code nesting. Now that you see how Sass is written, let’s look at some of the things that makes Sass so awesome. Variables $red: #FF4848 Nesting There are two types of nesting. Selector Nesting Mixins