Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models and Adaptive Boosting

by Jake Drew, PhD Introduction This article describes methods for machine learning using bootstrap samples and parallel processing to model very large volumes of data in short periods of time. The R programming language includes many packages for machine learningContinue reading… Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models and Adaptive Boosting

Man versus Artificial Intelligence: From Deep Blue to DeepMind in 20 Years

June 15, 2017 Garry Kasparov and DeepMind’s CEO Demis Hassabis discuss Garry’s new book “Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins ”, his chess match with IBM Deep Blue and his thoughts on the future of AIContinue reading… Man versus Artificial Intelligence: From Deep Blue to DeepMind in 20 Years

Here are 250 Ivy League courses you can take online right now for free

The 8 Ivy League schools are among the most prestigious colleges in the world. They include Brown, Harvard, Cornell, Princeton, Dartmouth, Yale, and Columbia universities, and the University of Pennsylvania. All eight schools place in the top fifteen of theContinue reading… Here are 250 Ivy League courses you can take online right now for free

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Abstract Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervisedContinue reading… Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Building meaningful machine learning models for disease prediction

Dr Shirin Glander Setup All analyses are done in R using RStudio. For detailed session information including R version, operating system and package versions, see the sessionInfo() output at the end of this document. All figures are produced with ggplot2.Continue reading… Building meaningful machine learning models for disease prediction

5 Benefits of Data and Analytics for Positive Business Outcomes

Today, businesses can collect data along every point of the customer journey. This information might include mobile app usage, digital clicks, interactions on social media and more, all contributing to a data fingerprint that is completely unique to its owner.Continue reading… 5 Benefits of Data and Analytics for Positive Business Outcomes

Making data science accessible – Machine Learning – Tree Methods

What are Tree Methods?  Tree methods are commonly used in data science to understand patterns within data and to build predictive models. The term Tree Methods covers a variety of techniques with different levels of complexity but my aim isContinue reading… Making data science accessible – Machine Learning – Tree Methods

How a Japanese cucumber farmer is using deep learning and TensorFlow

It’s not hyperbole to say that use cases for machine learning and deep learning are only limited by our imaginations. About one year ago, a former embedded systems designer from the Japanese automobile industry named Makoto Koike started helping outContinue reading… How a Japanese cucumber farmer is using deep learning and TensorFlow

Facebook’s advice to students interested in artificial intelligence

Math. Math. Oh and perhaps some more math.

That’s the gist of the advice to students interested in AI from Facebook’s Yann LeCun and Joaquin Quiñonero Candela

 who run the company’s Artificial Intelligence Lab and Applied Machine Learning group respectively.

Tech companies often advocate STEM (science, technology, engineering and math), but today’s tips are particularly pointed. The pair specifically note that students should eat their vegetables take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics as early as possible.

From this list, probability and statistics are perhaps the most interesting. From what I remember about high-school, those two subjects are regularly dismissed as too-obvious strategies for skirting the informal AP Calculus preference of top colleges and universities (AP Statistics is often thought of as a cop-out by students).

If differential equations represents the electricity that powers machine learning, statistics represents the gears of the machine itself — as the company touches on in a series of AI explainer videos we linked to at the bottom of this post.

To be fair, LeCun and Candela are most likely addressing the college crowd, though its important to consider incentives across all levels of education. Simply, we all could probably use some more statistics in our lives. Beyond math, the two say more math engineering, computer science, economics and neuroscience are also important subjects in today’s economy. How else would a fledgling machine learning student learn to leverage neuroeconomics and cognitive bias to target ads?

The pair also point to philosophy as a necessary prerequisite to understanding knowledge and learning. Amidst all the talk of News Feed bias, it’s important to remember that there is a human behind every application of machine learning. We don’t yet know how to escape the black box problem, but we do know that it will be humans working to figure it out and it would sure help if those humans understood how learning works before they start manipulating data.

Lastly, Facebook turns its attention to the actual mechanics of getting a job in the field of machine learning. Most of these tips are self-explanatory: find a professor to work with, consider working with PhD students who have more time on their hands and try to secure an industry-focused internship regardless of your future aspirations to understand how AI works in the real world.

When applying to PhD programs the two note that being able to identify a professor you want to work with is far more important than program ranking. Once there, students should work to address a specific problem and try to release a piece of open source code before all is said and done.

For more, click here.