Essay Sample on Unsupervised Learning

Introduction

Unsupervised learning refers to the kind machine learning that entails the lack of labels on the data that is supposed to be assessed, or that has been placed in a category using a predetermined procedure. All the examples, in this case, are not given any label, and that means one has to figure out how they are handled and come up with a final and perfect solution. This means that one does not have the based knowledge of the data there is nothing provided in the line to make thing possible. In this case, no evaluation is based on the accuracy that is obtained in the long run as there are appropriate algorithms.

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Some of the principles that guide the unsupervised form of machine learning are based on clustering, anomaly detection, and neural networks. In clustering, all sets of objects are a group in a manner that they will bear the same characteristics for a better understanding and processing. In that case, one can be able to carry out activities that cut across data mining, statistical data analysis, bioinformatics, image analysis, computer graphics, information retrieval, and data compression. In this case, it is important to note that all the process does not form an algorithm but have a task to be handled.

In the principle of anomaly detection, there is the conduction of analysis and investigation to find out some of the events and objects that do not add up or form part of the dataset. The anomalies are regarded as problems as they are supposed to be saved and utilized while taking part in further analysis (Marr, 2017). The anomalies, in this case, are also referred to as outlets, deviations, exceptions or noise. They are known to distort the order of data sets.

In the principle of artificial neural network, there are groups of interconnected networks that form nodes. The nodes are made in such a way that they will represent the artificial neuron. Some arrows are used to show the fact that there is an output that is going to become an input in the next stage or process. This means that all the activities take place independently and that is the reason why they are not supervised.

Some of the applications of unsupervised machine learning can be seen in the field of ecology where there is the recording of some audio files, and they are later on grouped according to the issue under study or whatever interests the person in the activity. The analysis of the recordings is then carried out using the unsupervised learning technique to be able to get whatever they are portraying when it comes to biodiversity (Larry, 2017). This will be used to understand the different types of sounds produced by birds concerning the region of interest.

Conclusion

The absence of labels is one of the challenges that can be observed in unsupervised learning. The other challenge is that one can identify the number of clusters that will be needed and also the method that they will incorporate to get the perfect results (Raschak, 2015). The other challenge of the technique is that it is likely to lead to random initializations which cause problems when it comes to analysis in the long run.

References

Larry, L. (2017). "CISO's Guide: Principles of Machine Learning." AirHeads.

Marr, B. (2017). "Supervised V Unsupervised Machine Learning - What's The Differnce." Forbes

Raschak, S. (2015). "What are some of the issues with unsupervised learning?"Quora.