An Introduction to WEKA - Machine Learning in Java WEKA (Waikato Environment for Knowledge Analysis) is an open source library for machine learning, bundling lots of techniques from Support Vector Machines to C4.5 Decision Trees in a single Java package. My examples in this article will be based on binary classification, but what I say is also valid for regression and in many cases for unsupervised learning. Why and when would you use a library? I'm not a fan of integrating libraries and frameworks just because they exist; but machine learning is something where you have to rely on a library if you're using codified algorithms as they're implemented more efficiently than what you and I can possibly code in an afternoon. Correctness is also a big deal: you can't be sure you have perfectly implemented the C4.5 algorithm for building decision trees just after reading the original paper twice. 1.J48 classifier = new J48(); 2.classifier.setOptions(new String[] { "-U" }); With respect to: 1.SVM classifier = new SMO(); 3. 01.double targetIndex;

Official VideoLectures.NET Blog » 100 most popular Machine Learning talks at VideoLectures.Net Enjoy this weeks list! 26971 views, 1:00:45, Gaussian Process Basics, David MacKay, 8 comments7799 views, 3:08:32, Introduction to Machine Learning, Iain Murray16092 views, 1:28:05, Introduction to Support Vector Machines, Colin Campbell, 22 comments5755 views, 2:53:54, Probability and Mathematical Needs, Sandrine Anthoine, 2 comments7960 views, 3:06:47, A tutorial on Deep Learning, Geoffrey E. Hinto3858 views, 2:45:25, Introduction to Machine Learning, John Quinn, 1 comment13758 views, 5:40:10, Statistical Learning Theory, John Shawe-Taylor, 3 comments12226 views, 1:01:20, Semisupervised Learning Approaches, Tom Mitchell, 8 comments1596 views, 1:04:23, Why Bayesian nonparametrics?, Zoubin Ghahramani, 1 comment11390 views, 3:52:22, Markov Chain Monte Carlo Methods, Christian P. Zoubin Ghahramani – ‘Internet search queries’ (Photo credit: Engineering at Cambridge)3018 views, 4:35:51, Graphical Models, Variational Methods, and Message-Passing, Martin J.

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.

hilarymason.com robots.stanford.edu/cs221/ What is this course about? What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of artificial intelligence (AI) is to tackle these with rigorous mathematical tools. In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Prerequisites: This course is fast-paced and covers a lot of ground, so it is important that you have a solid foundation on both the theoretical and empirical fronts. Homeworks (60%): There will be weekly homeworks with both written and programming parts. Written assignments: Homeworks should be written up clearly and succinctly; you may lose points if your answers are unclear or unnecessarily complicated. Although we recommend that you use a UNIX environment (e.g., Linux or OS X), the sanity check script grader.py should work on Windows as well.

Machine Learning Repository Machine Learning in Gradient Descent In Machine Learning, gradient descent is a very popular learning mechanism that is based on a greedy, hill-climbing approach. Gradient Descent The basic idea of Gradient Descent is to use a feedback loop to adjust the model based on the error it observes (between its predicted output and the actual output). Notice that we intentionally leave the following items vaguely defined so this approach can be applicable in a wide range of machine learning scenarios. The ModelThe loss functionThe learning rate Gradient Descent is very popular method because of the following reasons ... Batch vs Online Learning While some other Machine Learning model (e.g. decision tree) requires a batch of data points before the learning can start, Gradient Descent is able to learn each data point independently and hence can support both batch learning and online learning easily. In batch learning, all training will be fed to the model, who estimates the output for all data points. ɳ = ɳ_initial / (t ^ 0.5).

Solving Every Sudoku Puzzle by Peter Norvig In this essay I tackle the problem of solving every Sudoku puzzle. It turns out to be quite easy (about one page of code for the main idea and two pages for embellishments) using two ideas: constraint propagation and search. Sudoku Notation and Preliminary Notions First we have to agree on some notation. A puzzle is solved if the squares in each unit are filled with a permutation of the digits 1 to 9. That is, no digit can appear twice in a unit, and every digit must appear once. Every square has exactly 3 units and 20 peers. We can implement the notions of units, peers, and squares in the programming language Python (2.5 or later) as follows: def cross(A, B): "Cross product of elements in A and elements in B." return [a+b for a in A for b in B] It can't hurt to throw in some tests (they all pass): Now that we have squares, units, and peers, the next step is to define the Sudoku playing grid. Here is the code to parse a grid into a values dict: Constraint Propagation Search Why?

Reading and Text Mining a PDF-File in R 0inShare Here is an R-script that reads a PDF-file to R and does some text mining with it: clustering - How do you test an implementation of k-means? GATE.ac.uk - index.html 5 Principles for Applying Machine Learning Techniques - Factual Blog Here at Factual we apply machine learning techniques to help us build high quality data sets out of the gnarly mass of data that we gather from everywhere we can find it. To date we have built a collection of high quality datasets in the areas of places (local businesses and other points of interest) and products (starting with consumer packaged goods). In the long term, however, Factual is about perfecting the process of building data regardless of the area, so many of our techniques are domain agnostic. In this post, I cover 5 principles we use when putting machine learning techniques to work. 1. The biggest mistake people make when they attempt to use machine learning on data at huge volumes is ignoring the corner cases. The key is not giving up too soon. Think of Olympic sprinters. At Factual, we run this race toward high quality data in a pipeline with several stages we will describe in a later blog post. 2. Boundary cases are another area where we pay significant attention. 3. 4.

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