A Visual Introduction to Machine Learning Finding better boundaries Let's revisit the 73-m elevation boundary proposed previously to see how we can improve upon our intuition. Clearly, this requires a different perspective. By transforming our visualization into a histogram, we can better see how frequently homes appear at each elevation. While the highest home in New York is 73m, the majority of them seem to have far lower elevations. Your first fork A decision tree uses if-then statements to define patterns in data. For example, if a home's elevation is above some number, then the home is probably in San Francisco. In machine learning, these statements are called forks, and they split the data into two branches based on some value. That value between the branches is called a split point. Tradeoffs Picking a split point has tradeoffs. Look at that large slice of green in the left pie chart, those are all the San Francisco homes that are misclassified. The best split Recursion
Machine Learning - DZone - Refcardz From a probabilistic viewpoint, the predictive problem can be viewed as a conditional probability estimation; trying to find Y where P(Y | X) is maximized. From the Bayesian rule, P(Y | X) == P(X | Y) * P(Y) / P(X) This is equivalent to finding Y where P(X | Y) * P(Y) is maximized. Let's say the input X contains 3 categorical features— X1, X2, X3. In the general case, we assume each variable can potentially influence any other variable. P(X | Y) == P(X1 | Y) * P(X2 | Y) * P(X3 | Y), we need to find the Y that maximizes P(X1 | Y) * P(X2 | Y) * P(X3 | Y) * P(Y) Each term on the right hand side can be learned by counting the training data. But it is possible that some patterns never show up in training data, e.g., P(X1=a | Y=y) is 0. P(X1=a | Y=y) == (count(a, y) + 1) / (count(y) + m) …where m is the number of possible values in X1. When the input features are numeric, say a = 2.75, we can assume X1 is the normal distribution. Here is how we use Naïve Bayes in R:
Yun-Nung Chen Project 1: AutoCAD Drawing (2007) After taking some pictures, we used AutoCAD to draw the five views: front, left-side, right-side, rear, and plan views in detail. - Teamwork with Che-An Lu. * Rerults: [front], [left], [right], [rear], [plan], [report] Project 2: SketchUp Drawing (2007) According the pictures from project 1, we use SketchUp from Google to draw the 3D model. * Results: [image1], [image2], [image3], [report] Project 3: Blender Animation (2007) Using Blender to produce 3D models and make two short animation videos. - Teamwork with Che-An Lu. * Results: [image1], [video1], [video2], [report] Final Project: Animation Producing (2008) Using Blender to produce 3D models, plot a story, and make a refined animation videos. - Teamwork with Che-An Lu and Fang-Err Lin. * Results: [image1], [image2], [image3], [image4], [video] [report] * Award: selected as Best Final Project Award
Start Here Get Started and Get Good at Applied Machine Learning Hi, Jason here. I’m the guy behind Machine Learning Mastery. My goal is to help you get started, make progress and kick butt with machine learning. I teach a top-down and results-first approach designed for developers and engineers. This is unlike most academic textbooks and university courses. Access my best free tutorials on the blog or take the next step with my paid training material. You may be feeling overwhelmed. Take your time. Table of Contents What do you need help with? How Do I Get Started? The most common question I’m asked is: “how do I get started?” My best advice for getting started in machine learning is broken down into a 5-step process: For more on this top-down approach, see: Many of my students have used this approach to go on and do well in Kaggle competitions and get jobs as Machine Learning Engineers and Data Scientists. Applied Machine Learning Process For a good summary of this process, see the posts: R Machine Learning
Center for Machine Learning and Intelligent Systems | University of California, Irvine CognitiveJ – Image Analysis for Java | Ian's Blog CognitiveJ is an open source Java library that makes it easy to detect, interpret and identify faces or features contained within raw images. Powered by Project Oxford, The library can suggest a persons age, gender and emotional state. Based on machine learning, the library can also attempt to interpret and describe what is contained within an image. Its being released for public preview under the Apache 2 licence and at the time of writing, the features include; Faces Facial Detection with Age and Gender Vision Image Describe – Describe visual content of an image and return real world caption to what the image contains Image Analysis – Extract key details from an image and if the image is of an adult/racy natureOCR – Detect and extract a text stream from an imageThumbnail – Create thumbnail images based on key points of interest from an image Overlay Other Features Supports local and remote imagesValidation of parametersImage Grids Getting Started Pre-requisites Structure Wrappers Faces API
ConvNetJS: Deep Learning in your browser ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. Description The library allows you to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (I am a PhD student at Stanford). Common Neural Network modules (fully connected layers, non-linearities) Classification (SVM/Softmax) and Regression (L2) cost functions Ability to specify and train Convolutional Networks that process images An experimental Reinforcement Learning module, based on Deep Q Learning. Head over to Getting Started for a tutorial that lets you get up and running quickly, and discuss Documentation for all specifics. Code The code is available on Github under MIT license and I warmly welcome pull requests for new features / layers / demos and miscellaneous improvements. Discussion Group
7 Machine Learning Algorithms You Should Know Of : Tech : University Herald A machine learning algorithm is used in many ways to identify incorrect or correct data that is fed into the system. It is first given some sort of a "teaching set" of data, which is then used to answer a question. As more and more questions are asked, this new information is added to the algorithm making it smarter and better at performing its task over time. So one can say that these machines are "learning." Here are seven of the most common uses of this technology. Financial Trading Advertisement A lot of people want to find out what will happen to the stock market ahead of time. Data Security Malware is becoming a huge threat to data security. Medicine Machine learning algorithms can be used to detect risk factors for various diseases even before their human counterparts do. Fraud Detection Paypal, for example, uses a machine algorithm to prevent money laundering. Online Search Have you noticed how Google can be so accurate when it suggests words before you even complete typing them.
Resolving the Scope and Focus of Negation 24/05/2012: The data are available for download. 09/05/2012: The results and the list of accepted papers have been added. 03/04/2012: Updated information about the system description paper. 03/04/2012: The final version of the evaluation script and the results have been sent to participants. 16/03/2012: The test dataset has been distributed to participants. 15/03/2012: A new version of the evaluation script for the scope detection task has been distributed to participants. 13/03/2012: A new version of the evaluation script for the scope detection task has been distributed to participants. 09/03/2012: A new version of the CD-SCO dataset has been distributed to participants. 29/02/2012: A new version of the CD-SCO dataset has been distributed to participants. 22/02/2012: A new version of the CD-SCO dataset has been distributed to participants. 19/02/2012: The schedule has been changed. 17/02/2012: Evaluation script for the scope detection task has been released. Scope marks all negated concepts.