12 Machine Learning Articles to Catch You Up on the Latest Trend


Machine learning is a type of artificial intelligence that uses algorithms to iteratively learn from data and finds hidden insights in data without being explicitly programmed where to look or how to find the answer.

Here at SAS, we hear questions every day about machine learning: what it is, how it compares to other technologies, and why it matters. On the more technical end of the spectrum, we also hear questions about specific machine learning algorithms and how to use them. You will get all of your answers -- and more -- if you take the time to read the articles listed here, from definitions and comparisons to how-to tips on data science techniques.

This isn't a comprehensive list, of course, but I tried to pick some favorite articles I've seen published over the last year, and I'm trying to list them here in order from introductory to more advanced.

  1. Introduction to machine learning: Five things the quants wish we knew – Are you trying to talk about machine learning with someone on a different technical level? This article helps bridge that chasm. Read it, and see if you find it easier to talk to data scientists about machine learning afterwards.
  2. One button to help them all – Drivers and automotive manufacturers both stand to benefit from connected cars, and the data streaming from them. How will machine learning play a role in analyzing this data? Read to find out.
  3. Machine learning is crucial for fraud management – Fraud is becoming more complex every day, and the amount of data needed to identify fraud continues to grow. Machine learning is the perfect technology to help address the problem in every industry.
  4. Can advanced analytics for credit scoring change the mortgage market? – Equifax found that machine learning does a better job of predicting creditworthiness than previous methods. As a result, participating lenders can now safely lend money to more people.
  5. Machine learning + wearable medical devices = a healthier future for all – When healthcare providers can collect and analyze data from wearable devices, we all become healthier, and in-home health care becomes easier.
  6. Machine Learning: An invited guest to the IoT party? – Research indicates that IoT and Machine Learning are more valuable to utilities when used in combination but there are hurdles to overcome first. Learn more in this summar of recent research.
  7. Machine learning changes the way we forecast in retail and CPG – Learn how machine learning-based forecasting can analyze millions of products using unlimited amounts of causal factors simultaneously up and down a company's business hierarchy.
  8. Sentiment analysis, machine learning open up world of possibilities – When recovering from a natural disaster, officials could use machine learning and sentiment analysis to visualize patterns between social media updates and existing field data to better allocate resources and sharpen future preparedness efforts. And that's just one use case mentioned here.
  9. Deep learning methods and applicationsDeep learning is a type of machine learning that combines advances in computing power and special types of neural networks to learn complicated patterns in large amounts of data. Learn more about it in this interview.
  10. How intelligent machines learn to make sense of the world? – From supervised learning and unsupervised learning to reinforcement learning, understand the different types of machine learning and how they're used today.
  11. The difference between Statistical Modeling and Machine Learning, as I see it – For the Statistician the model comes first; for the Machine Learner the data are first, says SAS CTO Oliver Schabenberger. Read other comparisons in this post on LinkedIn.

From managers to data scientists and from retailers to health care professionals, machine learning is effecting us all. Read and share the articles here that matter most to your industry and your field, and let us know in the comments what other machine learning articles you've found useful too.

This content was reposted from the SAS Learning Post. Go there to view the original.

Alison Bolen, Editor of Blogs and Social Content

Alison Bolen is an editor at SAS, where she writes and edits blog content and publishes the Intelligence Quarterly magazine. She recently picked up and moved her family and her home office from Ohio to New York. Since starting at SAS in 1999, Alison has edited print publications, Web sites, e-newsletters, customer success stories and blogs. She has a bachelor's degree in magazine journalism from Ohio University and a master's degree in technical writing from North Carolina State University.

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Re: Resource
  • 4/4/2017 4:27:48 PM
NO RATINGS

..

Terry writes


Now, if only Alison had explained (or pointed us to an article) the difference between machine learning and AI, which I'm noticing people use interchangeably. An expert recently told me that AI is a subset of machine learning and more often used for machine-to-human interactions.


 

I found this to be a pretty informative and enlightening article that helps clarify the difference beteen AI and machine learning:

What's the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?

It seems to position machine learning as a subset of AI.

..

ML for Fraud
  • 3/27/2017 1:11:02 PM
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The topic of the third article is fraud - I see this as one of the best areas to apply ML.

The data is real-time. Fruad is like a needle in the haystack, so it's difficult to find no matter how you're looking. Fraud is expensive, in fact the article cites an estimate that fraud costs "5 percent of annual revenues worldwide" wow.

ML seems uniquely able to cope with this problem statement.

Re: Machine learning impacts
  • 3/24/2017 10:25:44 AM
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Yes, the first source was was a good explanation and emphasized it's still a collaboration between machine and human. Getting goat pictures confused with dogs, and tulips with balloons won't due but enter the human thought process and the fine tuning of the machine learning becomes workable.

Re: Machine learning impacts
  • 3/20/2017 2:02:15 PM
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Terry true, but the analysis is only as good as the data and the assumptions used. From the post-mortems, I have read the data used was old and based on previous behaviors, not current sentiment and the assumptions were also built on old data. As we always said garbage in and garbage out....

Re: Machine learning impacts
  • 3/20/2017 10:57:09 AM
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Seems like advances in machine learning could be used for improved polling and predicting election results more accurately. Even though it's been more than four months, the fact that so much data science still couldn't accurately predict the presidential election outcome is both hilarious and frustrating.

Re: Machine learning impacts
  • 3/20/2017 10:53:49 AM
NO RATINGS

Good points about linear/non-linear algorithms, Seth. Depending on the application or project, seems like it would be smart to cover both bases for maximum benefit, no?

Re: Resource
  • 3/20/2017 10:52:25 AM
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Yes, good point about compements, Michelle.

Now, if only Alison had explained (or pointed us to an article) the difference between machine learning and AI, which I'm noticing people use interchangeably. An expert recently told me that AI is a subset of machine learning and more often used for machine-to-human interactions. 

Resource
  • 3/19/2017 8:06:29 PM
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This a great resource for machine learning. IoT and machine learning go together like PB&J.

Re: Machine learning impacts
  • 3/16/2017 1:32:16 PM
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I agree. I really liked this article- it changed the way I view machine learning.

Re: Machine learning impacts
  • 3/15/2017 4:42:10 PM
NO RATINGS

Per the first article listed, it's interesting to learn that machine learning can be a black box for scientist and they may not totally comprehend the algorithms the machines are coming up with.  What really stands out is that the machine learning algorithims may not be linear or appear logical.    I guess that makes them more like people. 

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