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Comparing Supervised vs. Unsupervised Learning

#artificialintelligence

Technically speaking, the terms supervised and unsupervised learning refer to whether the raw data used to create algorithms has been prelabeled or not. In supervised learning, data scientists feed algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified in the training data. For example, if you are trying to train an algorithm to infer if a picture has a cat in it using supervised learning, data scientists create a label for each picture used in the training data indicating whether the image contains a cat or not. In an unsupervised learning approach, the algorithm is trained on unlabeled data.


Supervised Learning vs Unsupervised & Semi Supervised in One Picture

#artificialintelligence

Machine learning algorithms learn in three ways: unsupervised, supervised, and semi supervised. This picture illustrates the differences between the three types.


[D] Unsupervised-as-supervised learning • r/MachineLearning

#artificialintelligence

I'm including noise-contrastive estimation, and GANs, but I'm worried I won't have enough to write (need about 3000 words). I've gone through most of the citations for these papers, so I'm thinking of just including GAN variants (like f-GAN, WGAN etc) to fill out any additional space.


Understanding Supervised, Unsupervised, and Reinforcement Learning

#artificialintelligence

Once we start delving into the concepts behind Artificial Intelligence (AI) and Machine Learning (ML), we come across copious amounts of jargon related to this field of study. Understanding this jargon and how it can have an impact on the study related to ML goes a long way in comprehending the study that has been conducted by researchers and data scientists to get AI to the state it now is.


What Is Unsupervised Learning?

@machinelearnbot

The one point that I want to emphasize here is that the adjective "unsupervised" does not mean that these algorithms run by themselves without human supervision. It simply indicates the absence of a desired or ideal output corresponding to each input. An analyst (or a data scientist) who is training an unsupervised learning model has to exercise a similar kind of modeling discipline as does the one who is training a supervised model. Alternatively, an analyst who is training an unsupervised learning model can exercise a similar amount of control on the resulting output by configuring model parameters as does the one who is training a supervised model. While supervised algorithms derive a mapping function from x to y so as to accurately estimate the y's corresponding to new x's, unsupervised algorithms employ predefined distance/similarity functions to map the distribution of input x's.