Unsupervised machine learning methods find structure in unlabeled data.
They are used when we DON'T have prior knowledge about the categories.

Unsupervised statistical learning

Semi-supervised learning lies somewhere between supervised and unsupervised learning. It takes small amount of labeled data to start learning and then moves on to huge quantity of unlabaled data to develop further.

Semi Supervised learning

Differences about the Supervised vs Unsupervised Machine learning

Training a model using labeled data and using this model to predict the labels for new data is known as __________.

Modeling the features of an unlabeled dataset to find hidden structure is known as _____.

A time series is a set of measurements of a quantitative variable taken at sequential intervals over a continuous period of time. This approach allows researchers to track trends and ongoing changes within a specific domain, such as recording a factory's weekly worker productivity over an entire year.

Time Series

Supervised learning is a type of machine learning problem where the objective is to predict a designated unknown label based on known inputs. This approach requires a training dataset consisting of examples for which the ground truth labels are already known, allowing the model to learn the mapping from inputs to classifications.

Supervised Learning

- Supervised statistical learning
- Unsupervised statistical learning
- Time series learning
- Semi-Supervised learning

University of Michigan - Ann Arbor

Statistical learning involves using supervised tools, unsupervised tools, time series learning, and semi-supervised tools and methods to analyze data to understand the data, extract knowledge, and infer insights.

Statistical learning

https://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=CC82E9E0A5AA81DC8163412A904747D3?doi=10.1.1.99.9681&rep=rep1&type=pdf

Semi-Supervised Learning Literature Survey

- Parametric statistical methods
- Non-parametric statistical methods

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