Examples of Machine Learning Problems

Below are the 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.
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• 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.

• Churn Prediction:  Churn prediction is one of the most popular use cases for people who want to leverage machine learning. It has a large business value and benefit attached to itself specially in industries like the telecom and banking. Several challenges such as the skewed nature of the data set available and the ability to decide which models to use are going to be under a lot of debate.

• Sentiment Analysis : A lot of decisions these days are being taken on the opinion of others. We buy a product more because it has received a positive opinion and we visit a hotel most likely because it got the best rating online. Sentiment analysis has its own challenges such as how granular can the sentiment be determined, how subjective is the sentiment and so on, and hence sentiment analysis will be a good place to attack machine learning.

• News Aggregation: Plenty of news is being generated around us from various different places about a variety of topics. Yet we all have a constant thirst to consume all the news relevant to us as much as possible. How are we going to aggregate news according to the user's preference? Does his taste vary with time? How do we learn this variation? All this is going to be a challenge for machine learning and it involves a great deal of making sense of news and articles.

• Content Discovery/Search: There are millions of people around the world on various social networks and within enterprise. How can you discover people who share similar interests as yours and what parameters are you going to consider to measure this similarity? How do we measure similarity and can we quantify this? I feel this is a nice problem for machine learning where we will face the challenge of trying to find the needle in a haystack.

These above 10 or so examples give a good sense of what a machine learning problem looks like.