Real-Life applications of Machine Learning

What is Machine Learning? We can read authoritative definitions of machine learning, but really, machine learning is defined by the problem being solved. Therefore the best way to understand machine learning is to look at some example problem.

Examples of applications or problem statement

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.
  • Credit 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 codes hand written on envelops, identify the digit for each hand written character. A model of this problem would allow a computer program to read and understand handwritten zip codes and sort envelops 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 fulfil 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 has 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 behaviours 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 covert early or better engage in the trial.
  • Shape Detection: Given a user 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.

These 10 examples give a good sense of what a machine learning problem looks like. There is a corpus of historic examples, there is a decision that needs to be modelled and a business or domain benefit to having that decision modelled and efficaciously made automatically.

Some of these problems are some of the hardest problems in Artificial Intelligence, such as Natural Language Processing and Machine Vision (doing things that humans do easily). Others are still difficult, but are classic examples of machine learning such as spam detection and credit card fraud detection.

Variants of Machine Learning Problems

  • Regression: Data is labelled with a real value (think floating point) rather then a label. Examples that are easy to understand are time series data like the price of a stock over time, The decision being modelled is what value to predict for new unpredicted data.
  • Clustering: Data is not labelled, but can be divided into groups based on similarity and other measures of natural structure in the data. An example from the above list would be organising pictures by faces without names, where the human user has to assign names to groups, like iPhoto on the Mac.
  • Rule Extraction: Data is used as the basis for the extraction of propositional rules (antecedent/consequent aka if-then). Such rules may, but are typically not directed, meaning that the methods discover statistically supportable relationships between attributes in the data, not necessarily involving something that is being predicted. An example is the discovery of the relationship between the purchase of beer and diapers (this is data mining folk-law, true or not, it’s illustrative of the desire and opportunity).

When you think a problem is a machine learning problem, think next of what type of problem you could phrase it as easily or what type of outcome the client or requirement is asking for and work backwards.

Further Readings

  • The AI Effect: The notion where as soon as an Artificial Intelligence program achieves a good enough result it is no longer regarded as Artificial Intelligence, instead it is just technology and gets used in every day things. Applies just as equally to Machine Learning.
  • AI-Complete: refers to very difficult problems in Artificial Intelligence that if solved would be an example of Strong AI (AI as envisioned in science fiction, true AI). The problems of Computer Vision and Natural Language Processing are both examples of AI-Complete problems and may also be considered domain-specific categories of machine learning problems.
  • What are the Top 10 problems in Machine Learning for 2013? This Quora question has some excellent answers, and one that lists some broad categories of practical machine learning problems.

You have now understood the different real-world applications which can be implemented using ML Algorithms. Now, we can very well judge whether a problem is an ML problem statement or not, choose the variants to which it belongs.

Do you know of some more real-world machine learning problems? Leave a comment and share your thoughts.

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