Bayesian


How Cargo Cult Statistics encourages Deep Learning Alchemy

#artificialintelligence

There is a struggle today for the heart and minds of Artificial Intelligence. It's a complex "Game of Thrones" conflict that involves many houses (or tribes) (see: "The Many Tribes of AI"). The two waring factions I focus on today is those who practice Cargo Cult science in the form of Bayesian statistics and those who practice alchemy in the form of experimental Deep Learning. For the uninitiated, let's talk about what Cargo Cult science means. Cargo Cult science is a phrased coined by Richard Feynman to illustrate a practice of in science of not working from fundamentally sound first principles.


Why Probability Theory Should be Thrown Under the Bus

@machinelearnbot

So, what's Yann LeCun talking about when he says "he's ready to throw Probability Theory under the bus"? This article attempts to explore this sentiment. The problem with Probability Theory has to do with its efficacy in making predictions. It's obvious that the distributions are different, unfortunately the statistical measures are identical! Said differently, if the basis of your predictions are expectations calculated from probability distributions, then you can very easily be fooled.



Can small companies successfully implement deep learning?

@machinelearnbot

The world of deep learning is dominated by academics and technology giants pumping thousands of dollars into their research and applications every day. There are so many real-world problems that can be solved by DL that huge corporations aren't solving. There are countless startups trying to solve an array of issues and improve efficiency in countless industries, and many of these fail - not due necessarily to a poor idea or execution, but they are often unfunded and understaffed. The startups with the really extraordinary ideas however, often secure funding from Venture Capitalists, in crowdfunding campaigns, or through awards or grants. The CEOs of these companies are not necessarily AI experts, but are experts in their own industry from artists, to healthcare professionals, scientists, retail managers and many more.


R Weekly 2017-42 RStudio 1.1, Growth of R

@machinelearnbot

How are people doing bayesian statistics in 2017? Enabling Concerned Visitors & Ethical Security Researchers with security.txt Feels like a dry winter – but what does the data say? Support for a system Java on macOS has been removed - install a fairly recent Oracle Java (see R Installation and Administration §C.3.2). R CMD javareconf has been updated to recognize the use of a Java 9 SDK on macOS.


playlist?list=PLAwxTw4SYaPl0N6-e1GvyLp5-MUMUjOKo

#artificialintelligence

This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree. The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff. This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS).


Machine Learning:Supervised Learning Part 1a of 3 - YouTube

#artificialintelligence

This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree. The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff. This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS).



Machine Learning for Investors: A Primer -

@machinelearnbot

If you believe any of your standard errors, you take an opinionated view that the underlying data fit a linear model plus a normally distributed error. For instance, what is the nonlinear model that best approximates the data, where'best' means it uses the number of degrees of freedom that makes it optimally predictive out-of-sample? In machine learning, we do numerical optimizations, whereas in old-school statistics we solved a set of equations based on an opinionated view of what'clean' data look like. A single-cell neural network with sigmoid activation performs logistic regression and creates a linear decision boundary.6 Now add a second cell.


Improving your statistical inferences Coursera

@machinelearnbot

First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses.