Give a definition of a learning system. Learning is anypractice by which a system improves Performance from experience. For example,let take school as an example. School have teacher, books and other resources.Students they have they get experience from their teacher, books and otherresources and as a result of this they improve their performance. What are the basic components at thelearning system? Basic components I. Performance II.
Task III. ExperiencesThere are three different tasks inmachine learning (ML): supervised learning, unsupervised learning, andreinforcement learning. How are they different? Supervise means to observe anddirect the execution of a task. Means supervising a machine learning model thatmight be able to produce classification regions. Teach the model by training itwith some data from a labeled dataset than load the model with knowledge sothat we can have it predict future instances. Generally speaking, the model istrained on a labeled dataset, so it can predict the outcome of out of sampledata.There is 2 type of supervised learning: classification and regression.
Unsupervised learning is exactly asit sounds, let the model work on its own to discover information that may notbe visible for our eyes. It uses machine learning algorithms that extractconclusions on unlabeled data. Unsupervised learning has more difficult algorithmsthan supervised learning, since we know little to no information about thedata, or the outcomes that are to be expected. With unsupervised learning,we’re looking to find things such as group, clusters perform density estimationand dimensionality reduction. In supervised learning, however, we know whatkind of data we’re dealing with, since it is labelled data.In comparison to supervisedlearning, unsupervised learning has: fewer tests and fewer model that can beused in order to ensure the outcome of the model is accurate. As such unsupervisedlearning create a less controllable environment, as the machine is creating outcomesfor us.
The biggest difference between supervised and unsupervised learning isthat supervised learning deals with labeled data while unsupervised learningdeals with unlabeled data. In supervised learning, we have machine learningalgorithms for classification, and regression. Classification is the organizationof labeled data and regression is the prediction of trends in labeled data todetermine future outcomes. In unsupervised learning, we have clustering. Clusteringis the analysis of patterns and groupings of unlabeled data.