SurfStat
Posted by Armando Brito Mendes | Filed under estatística, lições, materiais ensino
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Um bom livro on-line com apps e muito mais…
| SURFSTAT australia |
| This site has already benefited from the contributions of many people. Please do your bit and let us know of errors, missing topics or things you think could be better explained. |
Tags: análise de dados, Estat Descritiva
Random Probability, Mathematical Statistics, Stochastic Processes
Posted by Armando Brito Mendes | Filed under estatística, lições, software
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Um site organizado por temas como um livro com apps interessantes
Welcome!
Random (formerly Virtual Laboratories in Probability and Statistics) is a website devoted to probability, mathematical statistics, and stochastic processes, and is intended for teachers and students of these subjects. The site consists of an integrated set of components that includes expository text, interactive web apps, data sets, biographical sketches, and an object library. Please read the Introduction for more information about the content, structure, mathematical prerequisites, technologies, and organization of the project. Random is hosted at two sites: www.math.uah.edu/stat/ and www.randomservices.org/stat/. For updates, please follow @randomservices on Twitter.
Basic Information
Expository Chapters
- Foundations
- Probability Spaces
- Distributions
- Expected Value
- Special Distributions
- Random Samples
- Point Estimation
- Set Estimation
- Hypothesis Testing
- Geometric Models
- Bernoulli Trials
- Finite Sampling Models
- Games of Chance
- The Poisson Process
- Renewal Processes
- Stochastic Processes
- Markov Chains
- Brownian Motion
Ancillary Materials
Support and Navigation
Tags: software estatístico
What WWW Data Sources Do STUDENTS Choose?
Posted by Armando Brito Mendes | Filed under data sets, estatística, lições
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Conjuntos de dados usados numa cadeira com um projeto semelhante a P&E
Here are some of the links to data found by students for projects in Robin Lock’s courses at St. Lawrence University
Note: Some links may no longer be current.
Non-Sports Themes
- Crimes in Albert Park – monthly (Ryan Audet)
- Ice Cream Consumption (Caroline Jenkins)
- Heart Transplants (Laura Hostetter)
- College Ratings (Katie Morgan)
- Faculty Salaries (Erica Rapp)
- State Expenditures (Christopher Morin)
- Places Rated Almanac (Kiley Stoops)
Sports Themes (Note: Data may change as new seasons occur)
- Major League Baseball – Hitters (Kate Achilles)
- Boston Red Sox and NY Yankee Hitting (Jason Kellogg)
- NCAA Basketball – Teams (Jessica Burnham)
- NCAA Basketball – Players (Jessie Waldeier)
- NBA Scoring Leaders (Anna Ryan)
- NHL Scoring Leaders – NHL.com (Dean DiMarco & Jake Harney)
- NHL Scoring Leaders – CNN/SI (Brian LaBombard)
- NHL – Chicago Blackhawks (Toni Maloney)
- Kentucky Derby (Melissa Fleischhauer & Greg Jones)
- Men’s and Women’s Gymnastic World Championships (Page Wages)
- Women’s Tennis Rankings (Jessica Mainelli)
- Winter Olympic Medals (Katie Wears)
- Surfing Competition (Courtney Shay)
- more…
Tags: data mining, open data
Chance Lecture Video Series
Posted by Armando Brito Mendes | Filed under estatística, lições, materiais ensino
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Bons vídeos ainda q antigos de alguns temas em probabilidade e estatística
This page has links to:
The talks featured below require the latest version of the Realplayer software. More particularly, they require that the “Realplayer plug-in” be installed in the plug-ins folder of your browser. If you do not have the “Realplayer plug-in,” a free version of Realplayer (which includes the plug-in) is available here
Tags: inferência, problemas
Theoretical Motivations for Deep Learning
Posted by Armando Brito Mendes | Filed under lições
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Uma boa introdução ao Deep Learning uma nova técnica de machine learning.
This post is based on the lecture “Deep Learning: Theoretical Motivations” given by Dr. Yoshua Bengio at Deep Learning Summer School, Montreal 2015. I highly recommend the lecture for a deeper understanding of the topic.
Deep learning is a branch of machine learning algorithms based on learning multiple levels of representation. The multiple levels of representation corresponds to multiple levels of abstraction. This post explores the idea that if we can successfully learn multiple levels of representation then we can generalize well.
Tags: data mining, machine learning
