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Discussion points:
- Unsupervised learning doesn't have a correct answer i.e. you can't be more or less accurate in the output
- Learn "hidden" structure in data
- Clustering is classical example
- Group like things together
- Example use case: movie database with people's preferences, you want to cluster and see different types of people
- Example use case: grouping documents or articles of similar content
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What is Unsupervised Learning?
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
Types:
Clustering: Is used for exploratory data analysis to find hidden patterns or grouping in data. Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.
Approaches to unsupervised learning include:
clustering. k-means. mixture models. hierarchical clustering,anomaly detection.Neural Networks. Hebbian Learning. Generative Adversarial Networks.Approaches for learning latent variable models such as. Expectation–maximization algorithm (EM) Method of moments
Few more Examples:
Suppose you have data for an E-commerce site. You have a list of people and things they have ordered online this last week. You can now use Clustering Algorithms and find the pattern in the data, predict the buying trend and formulate the business strategy as per the trend.
