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CS 229: Machine Learning (Course handouts)

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.

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Maximum Entropy Modeling This page dedicates to a general-purpose machine learning technique called Maximum Entropy Modeling (MaxEnt for short). On this page you will find: What is Maximum Entropy Modeling In his famous 1957 paper, Ed. T. Introduction to Machine Learning Draft of Incomplete Notes by Nils J. Big Data and Social Analytics certificate course Earn an MIT certificate in the fundamental theory and analysis of big data to better understand and predict human networks and behaviours in social structures. Conduct preliminary analysis and draw hypotheses about that data Understand exactly what kind of data you are dealing with in your role Learn Minecraft Hour of Code Grades 2+ | Blocks Moana: Wayfinding with Code Make a Flappy game Data Visualization App Using GAE Python, D3.js and Google BigQuery: Part 3 - Tuts+ Code Tutorial In the previous part of this tutorial, we saw how to get started with D3.js, and created dynamic scales and axes for our visualization graph using a sample dataset. In this part of the tutorial, we'll plot the graph using the sample dataset. To get started, clone the previous tutorial source code from GitHub. Navigate to the Google App Engine (GAE) SDK directory and start the server. Point your browser to and you should be able to see the X and Y axes that we created in the previous tutorial.

Octave Programming Tutorial From the Octave website: Octave is a high-level language, primarily intended for numerical computations. It provides a convenient command line interface for solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with Matlab. It may also be used as a batch-oriented language.Octave has extensive tools for solving common numerical linear algebra problems, finding the roots of non-linear equations, integrating ordinary functions, manipulating polynomials, and integrating ordinary differential and differential-algebraic equations. It is easily extensible and customizable via user-defined functions written in Octave's own language, or using dynamically loaded modules written in C++, C, Fortran, or other languages. The purpose of this collection of tutorials is to get you through most (and eventually all) of the available Octave functionality from a basic level.

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 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!

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