Probability and Monte Carlo methods
Posted by Armando Brito Mendes | Filed under estatística, Habilitações Académicas, matemática, materiais ensino
Um bom texto de introdução à probabilidade e simulação de Monte-Carlo
This is a lecture post for my students in the CUNY MS Data Analytics program. In this series of lectures I discuss mathematical concepts from different perspectives. The goal is to ask questions and challenge standard ways of thinking about what are generally considered basic concepts. I also emphasize using programming to help gain insight into mathematics. Consequently these lectures will not always be as rigorous as they could be.
Tags
monte carlo, numerical integration, probability, simulation
Tags: Estat Descritiva, R-software, software estatístico
noticias, textos e tudo o mais sobre big data
Posted by Armando Brito Mendes | Filed under materiais para profissionais
muito material interessante sobre big data
Tags: big data, data mining, DW \ BI
A Wakanow Guide to Geography
Posted by Armando Brito Mendes | Filed under visualização
Um web-book sobre cartografia e SIG
The Different Stages of Mapmaking
The Compass as a Mapping Device
Navigating with the Celestial Bodies
Follow these links to learn more about cartography:
- Cartography Concepts: A Student’s Guide to Mapmaking
- PBS: Mapmaking Classroom Resources
- The Mathematics of Cartography: History of Mapmaking
- National Geographic: Mapmaking Guide (6-8) (PDF)
- The University of Texas Libraries: Glossary of Cartographic Terms
- The University of Wisconsin-Madison: The History of Cartography
- Brief History of Maps and Cartography
- Henry-Davis: Cartographic Images
- The Art of Cartography
- The University of Nebraska-Omaha: What is Cartography?
- What is Cartography: Types of Maps
- The World’s Most Useful Online Map Database: Modern Map Collection: Political, Physical, Thematic, Outline
- How Cartographers Use Symbols (PDF)
- Harvard University: Elements of Cartographic Style
- The University of Texas-Arlington: Maps, Atlases, and Cartographic Collections
- HowStuffWorks: How Maps Work
- Cartography and Geographic Information Society
- The International Cartographic Association
- Ball State University: Maps and Cartography (PDF)
- The University of Georgia Libraries: Online Cartographic Resources
Machine Learning
Posted by Armando Brito Mendes | Filed under materiais ensino, videos
Um bom curso video de machine learning
About the Course
Course Syllabus
Tags: data mining, DW \ BI
Machine Learning MOOC
Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, videos
Um curso muito completo de machine learning
About the Course
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
FAQ
- What is the format of the class?The class will consist of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures, and programming assignments.
- How much programming background is needed for the course?The course includes programming assignments and some programming background will be helpful.
- Do I need to buy a textbook for the course?No, it is self-contained.
- Will I get a statement of accomplishment after completing this class?Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructor.
Tags: big data, bioinformatica, captura de conhecimento, data mining, DW \ BI
Data Mining with Weka MOOC
Posted by Armando Brito Mendes | Filed under Habilitações Académicas, materiais ensino, software, videos
Um curso em vídeo sobre a utilização do WEKA para data mining
Welcome to the free online course Data Mining with Weka
This 5 week MOOC introduced data mining concepts through practical experience with the free Weka tool.
The course featured:
- video lectures by Professor Ian H. Witten
- the open-source Weka data mining platform
- access to chapters from Data Mining (3rd Edition)
- discounts from Morgan Kaufmann
- online assessment leading to a statement of completion
The course will run again in early March 2014. To get notified about dates (enrolment, commencement), please subscribe to the announcement forum.
You can access the course material (videos, slides, etc) from here.
Tags: data mining, software estatístico, WEKA
Why Predictive Modelers Should be Suspicious of Statistical Tests
Posted by Armando Brito Mendes | Filed under estatística
Um excelente exemplo de correlações espúrias
Well, the danger is really not the statistical test per se, it the interpretation of the statistical test.
Yesterday I tweeted (@deanabb) this fun factoid: “Redskins predict Romney wins POTUS #overfit. if Redskins lose home game before election => challenger wins (17/18) http://www.usatoday.com/story/gameon/2012/11/04/nfl-redskins-rule-romney/1681023/” I frankly had never heard of this “rule” before and found it quite striking. It even has its own Wikipedia page (http://en.wikipedia.org/wiki/Redskins_Rule).
For those of us in the predictive analytics or data mining community, and those of us who use statistical tests to help out interpreting small data, 17/18 we know is a hugely significant finding. This can frequently be good: statistical tests will help us gain intuition about value of relationships in data even when they aren’t obvious.
Tags: data mining, IBM SPSS Statistics, inferência, software estatístico
8 Mistakes Our Brains Make
Posted by Armando Brito Mendes | Filed under ADM - multicritério, materiais para profissionais
A tomada de decisão intuitiva cai frequentemente em armadilhas
1. We surround ourselves with information that matches our beliefs
2. We believe in the “swimmer’s body” illusion
3. We worry about things we’ve already lost
4. We incorrectly predict odds
5. We rationalize purchases we don’t want
6. We make decisions based on the anchoring effect
7. We believe our memories more than facts
8. We pay more attention to stereotypes than we think
Tags: decisao em grupo
blog: Notícias de Gestão de Projetos
Posted by Armando Brito Mendes | Filed under planeamento
Um bom blog sobre gestão de projetos
Tags: gestão de projetos
Engineer solves Big Data Conjecture
Posted by Armando Brito Mendes | Filed under Investigação Operacional, matemática
Um problema de combinatória em contexto de big-data
IBM Distinguished Engineer solves Big Data Conjecture
A mathematical problem related to big data was solved by Jean-Francois Puget, engineer in the Solutions Analytics and Optimization group at IBM France. The problem was first mentioned on Data Science Central, and an award was offered to the first data scientist to solve it.
Bryan Gorman, Principal Physicist, Chief Scientist at Johns Hopkins University Applied Physics Laboratory, made a significant breakthrough in July, and won $500. Jean-Francois Puget completely solved the problem, independently from Bryan, and won a $1,000 award.
Tags: big data, data mining, otimização
