Cumberland University Supervised and Unsupervised Learning Questions

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I. Supervised and Unsupervised learning: Explain the differences between the two main types of machine learning methods. What are the main categories of each learning method?

II. Artificial Neural Nets: Describe how artificial neural nets (ANNs) use supervised learning to predict outcomes in decision-making. How are ANNs employed as data analysis tools for forecasting in the realm of managerial decision support?

III. Real-world Examples: Provide a real-world example of each type of learning and explain how each method is applied in each of your examples. You should discuss one example of supervised learning and one example of unsupervised learning in this section of the essay.

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Outline Supervised and Unsupervised Learning
I.

Differences and Categories of Supervised and Unsupervised Learning
✓ Supervised learning is a type of machine learning method where the machine is trained
using well-labeled data, meaning some of the data is already placed in the correct format.
✓ The unsupervised machine learning method uses algorithms that allow the machine to
work on its own with unlabeled data.
✓ The primary working mechanism for supervised learning is the optimization of data using
experience.
✓ The primary working mechanism for unsupervised learning, the machine locates all the
unknown trends in the data.

✓ Unsupervised machine learning techniques include association and clustering.
II. Artificial Neural Nets
✓ These play the role of quantitative models in decision making support systems.
✓ To help in the realm of managerial support for Hollywood managers, ANN uses MLP
neural network with two concealed layers and several assigned variables to them.
✓ ANNs are used in say movie forecast framework in web browsers and relayed to a user.
✓ The use of Multilayered Perceptron can learn complex arbitrary non-linear function to a
precise absolute level.
III.

Real-World Examples
✓ One key example of this type of machine learning method is the evaluation or
prediction of time taken for a person to reach home from their workplace or shopping
mall.

✓ For unsupervised classification, the scenarios change since they are no training data.
The method may be used to determine the reaction of a child with their family dog
and seeing a neighbor's dog.
IV.

Conclusion
✓ Supervised and unsupervised learning methods are both fundamental in assessing
situations and coming up with predictive models


Running head: SUPERVISED AND UNSUPERVISED LEARNING

Supervised and Unsupervised Learning
Name
Course
Tutor
Date

1

SUPERVISED AND UNSUPERVISED LEARNING

2

Differences and Categories of Supervised and Unsupervised Learning
Supervised learning is a type of machine learning method where the machine is trained
by the use of well-labeled data, meaning some of the data is already placed in the correct format.
The algorithms used learn data from the labeled training data to make predictions for the
unforeseen data (Ayodele, ...


Anonymous
Really useful study material!

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