ONLINE OPEN-ACCESS TEXTBOOKS Search form You are here Forecasting: principles and practice Rob J Hyndman George Athanasopoulos Statistical foundations of machine learning Gianluca Bontempi Souhaib Ben Taieb Electric load forecasting: fundamentals and best practices Tao Hong David A. Modal logic of strict necessity and possibility Evgeni Latinov Applied biostatistical analysis using R Stephen B. Introduction to Computing : Explorations in Language, Logic, and Machines David Evans Over 100 Incredible Infographic Tools and Resources (Categorized) - DailyTekk This post is #6 in DailyTekk’s famous Top 100 series which explores the best startups, gadgets, apps, websites and services in a given category. Total items listed: 112. Time to compile: 8+ hours. Update: Be sure to check out our latest post on infographics: Infographics Are Everywhere – Here’s How to Make Yours Go Viral. I love a good infographic! You might also like: Post Navigation The Best Blogs and Websites about Infographics Visual.ly – Awesome community for creating and sharing infographics.Information Aesthetics – The relationship between design and information.Visualizing.org – Making sense of complex issues through data and design.Visual Complexity – A resource for the visualization of complex networks.Daily Infographic – A new infographic every day.GOOD Infographics – GOOD Magazine’s excellent infographics section.Information Is Beautiful – Ideas, issues, knowledge, data – visualized.Infographic of the Day – ... There’s more to this article!
Difference Between Data Mining VS Predictive Analytics VS Machine Learning etc If you are a beginner in data mining and want to become at least familiar with the main concepts and terminologies, maybe the first step would be to acquire a clear bird’s eye view about the whole domain – definition, inception, classification, influences, trends – but without diving into too deep and scholastic details. And, of course, you do that by searching on Internet and skimming whatever books you have at hand. You might say that one day would be more than enough to get an overall understanding about this domain. But you don’t have a clue about the trouble you’re getting into. This article wants to shed some light on this questions and present all these concepts in a very simple manner. Data Mining Definition Let’s quickly start with a definition, of course. Data Mining Inception Unfortunately, we don’t have a clear date to celebrate data mining birthday every year. The annual Bill of Mortality for London and its environs, 1665 Influences Here’s where the pain starts. What is KDD?
HoTT/book Mining of Massive Datasets The book has now been published by Cambridge University Press. The publisher is offering a 20% discount to anyone who buys the hardcopy Here. By agreement with the publisher, you can still download it free from this page. Cambridge Press does, however, retain copyright on the work, and we expect that you will obtain their permission and acknowledge our authorship if you republish parts or all of it. --- Jure Leskovec, Anand Rajaraman (@anand_raj), and Jeff Ullman Download Version 2.1 The following is the second edition of the book, which we expect to be published soon. There is a revised Chapter 2 that treats map-reduce programming in a manner closer to how it is used in practice, rather than how it was described in the original paper. Version 2.1 adds Section 10.5 on finding overlapping communities in social graphs. Download the Latest Book (511 pages, approximately 3MB) Download chapters of the book: Download Version 1.0 Download the Book as Published (340 pages, approximately 2MB)
Glossary / Mathematics and statistics Statistics This glossary describes terms used in the achievement objectives of the statistics strand of the curriculum, as well as other related terms. Many of these terms have other meanings when used in other contexts. Terms in this glossary that appear in another description are italicised when they are used for the first time. In descriptions of terms from the probability thread, events are in bold. Some terms have equivalent names listed under ‘Alternative’, and closely related terms are listed under ‘See’. The terms provide references to levels in the statistics strand achievement objectives. Download the full glossary of terms:
Knowledge Sharing Tools and Methods Toolkit - home Universal Properties Previously in this series we’ve seen the definition of a category and a bunch of examples, basic properties of morphisms, and a first look at how to represent categories as types in ML. In this post we’ll expand these ideas and introduce the notion of a universal property. We’ll see examples from mathematics and write some programs which simultaneously prove certain objects have universal properties and construct the morphisms involved. A Grand Simple Thing One might go so far as to call universal properties the most important concept in category theory. Definition: An object in a category is called initial if for every object there is a unique morphism . is called final if for every object . In both cases, the existence of a unique morphism is the same as saying the relevant Hom set is a singleton (i.e., for initial objects , the Hom set consists of a single element). when In the single element set is final, but not initial; there is only one set-function to a single-element set. Proposition: If
An R "meta" book by Joseph Rickert I am a book person. I collect books on all sorts of subjects that interest me and consequently I have a fairly extensive collection of R books, many of which I find to be of great value. Recently, however, while crawling around CRAN, it occurred to me that there is a tremendous amount of high quality material on a wide range of topics in the Contributed Documentation page that would make a perfect introduction to all sorts of people coming to R. The content column lists the topics that I think ought to be included in a good introductory probability and statistics textbook. Finally, I don’t mean to imply that the documents in my table are the best assembled in the Contributed Documentation page.
It is so random! Or is it? The meaning of randomness First there is the problem of lexical ambiguity. There are colloquial meanings for random that don’t totally tie in with the technical or domain-specific meanings for random. Then there is the fact that people can’t actually be random. Then there is the problem of equal chance vs displaying a long-term distribution. And there is the problem that there are several conflicting ideas associated with the word “random”. In this post I will look at these issues, and ask some questions about how we can better teach students about randomness and random sampling. Lexical ambiguity Representations of the different meanings of the word, random. So what are the different meanings for random? Different meanings Without method The first meaning of random describes something happening without pattern, method or conscious decision. Statistical meaning Most on-line dictionaries also give a statistical definition, which includes that each item has an equal probability of being chosen. Informal or colloquial
60+ educational resources for teaching yourself anything. From its inception, the web has always had appeal as an educational resource. Recognising the potential for remote learning, in 2002, the launch of OpenCourseWare at MIT helped propel the initiative into the spotlight, with many universities following suit and providing quality educational material available through the web. No longer is there an excuse for anyone with access to the web to say that education is outside of their reach. This collection of links and applications highlights just the tip of the iceberg of educational resources that are available on the web. If you are interested in teaching yourself a new skill or learning a new topic indepth in your spare time, hopefully some of these will be of use. University Material Open Yale - Open Yale Courses provides free and open access to a selection of introductory courses taught by distinguished teachers and scholars at Yale University. Open Courseware – Notre Dame University contribution to open courseware. Video Material Courses