Examples of Machine Learning
Machine Learning is employed in a range of computing tasks where designing and programming explicit algorithms is unfeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining,where the latter sub-field focuses more on exploratory data analysis and is known as unsupervised learning.
Machine learning can be thought of as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data). Machine learning is usually divided into two main types : (i) predictive or supervised learning approach, and (ii) descriptive or unsupervised learning approach. There is also a third type known as reinforcement learning, which is somewhat less commonly used. [1]
Contents
10 Examples of Machine Learning Problems
Machine Learning problems are abound. They make up the core or difficult parts of the software you use on the web or your desktop every day. Think of the “do you want to follow” suggestions on twitter and the speech understanding in Apple’s Siri.
Below are 10 examples of machine learning that really ground what machine learning is all about.
Spam Detection: Given email in an inbox, identify those email messages that are spam and those that are not. Having a model of this problem would allow a program to leave non-spam emails in the inbox and move spam emails to a spam folder. We should all be familiar with this example.
Card Fraud Detection: Given credit card transactions for a customer in a month, identify those transactions that were made by the customer and those that were not. A program with a model of this decision could refund those transactions that were fraudulent.
Digit Recognition: Given a zip code handwritten on envelops, identify the digit for each handwritten character. A model of this problem would allow a computer program to read and understand handwritten zip codes and sort envelopes by geographic region.
Speech Understanding: Given an utterance from a user, identify the specific request made by the user. A model of this problem would allow a program to understand and make an attempt to fulfill that request. The iPhone with Siri has this capability.
Face Detection: Given a digital photo album of many hundreds of digital photographs, identify those photos that include a given person. A model of this decision process would allow a program to organize photos by person. Some cameras and software like iPhoto have this capability.
Product Recommendation: Given a purchase history for a customer and a large inventory of products, identify those products in which that customer will be interested and likely to purchase. A model of this decision process would allow a program to make recommendations to a customer and motivate product purchases. Amazon has this capability. Also, think of Facebook, GooglePlus, and Facebook that recommend users to connect with you after you sign-up.
Medical Diagnosis: Given the symptoms exhibited in a patient and a database of anonymized patient records, predict whether the patient is likely to have an illness. A model of this decision problem could be used by a program to provide decision support to medical professionals.
Stock Trading: Given the current and past price movements for a stock, determine whether the stock should be bought, held or sold. A model of this decision problem could provide decision support to financial analysts.
Customer Segmentation: Given the pattern of behaviour by a user during a trial period and the past behaviors of all users, identify those users that will convert to the paid version of the product and those that will not. A model of this decision problem would allow a program to trigger customer interventions to persuade the customer to convert early or better engage in the trial.
Shape Detection: Given a user a hand drawing a shape on a touch screen and a database of known shapes, determine which shape the user was trying to draw. A model of this decision would allow a program to show the platonic version of that shape the user drew to make crisp diagrams. The Instaviz iPhone app does this. [2]
Case studies of machine learning in use
- Microsoft incorporates machine learning in Bing to improve relevance of search results. [3]
- IBM Watson uses machine learning to solve business and research problems. [4]
- Cornell University is investigating use of machine learning to identify whales so ships can avoid hitting them. [5]
- Bloomberg Uses machine learning for accurate financial information. [6]
- BillGuard is a personal finance security company that tracks all transactions made. They use machine learning algorithms to identify any fraudulent activities. [7]
- Kaggle is the world's largest data science community. Companies and researchers post their data. Statisticians and data miners from all over the world compete to produce the best models. [8]
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Reference
- ↑ https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf
- ↑ http://machinelearningmastery.com/practical-machine-learning-problems/
- ↑ http://blogs.technet.com/b/machinelearning/archive/2014/07/11/machine-learning-for-industry-a-case-study.aspx
- ↑ http://www.ibm.com/smarterplanet/us/en/ibmwatson/what-is-watson.html
- ↑ http://www.forbes.com/sites/85broads/2014/01/06/six-novel-machine-learning-applications/
- ↑ http://letstalkpayments.com/15-players-that-use-machine-learning-in-fintech-space/
- ↑ http://letstalkpayments.com/15-players-that-use-machine-learning-in-fintech-space/
- ↑ https://angel.co/kaggle/
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