Ever since the intersection of lightning-fast hardware and brilliant software, machines have been learning how to think like humans. Data scientists use many different kinds of machine learning algorithms to discover patterns in big data that lead to actionable insights. At a high level, these different algorithms can be classified into two groups based on the way they “learn” about data to make predictions: supervised and unsupervised learning.  Explain the differences between the two main types of machine learning methods.  Describe how artificial neural nets (ANNs) use supervised learning to predict outcomes in decision-making.  Provide one real-world example of how each type of learning is applied in data science.  

ITS 531 Module Three Essay Guidelines and Rubric

Topic: Machine Learning

Overview:

Ever since the intersection of lightning-fast hardware and brilliant software, machines have been learning how to think like humans. Data
scientists use many different kinds of machine learning algorithms to discover patterns in big data that lead to actionable insights. At a high
level, these different algorithms can be classified into two groups based on the way they “learn” about data to make predictions: supervised and
unsupervised learning.

 Explain the differences between the two main types of machine learning methods.
 Describe how artificial neural nets (ANNs) use supervised learning to predict outcomes in decision-making.
 Provide one real-world example of how each type of learning is applied in data science.

Guidelines for Submission: Using APA 6th edition style standards, submit a Word document that is 2-3 pages in length (excluding title page,
references, and appendices) and include at least two credible scholarly references to support your findings. The UC Library is a good place to find
these sources. Be sure to cite and reference your work using the APA guides and essay template that are located in the courseroom.

Include the following critical elements in your essay:

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.

Required elements:
 Please ensure your paper complies APA 6th edition style guidelines. There is an essay template located under the Course Resources link.
 APA basics:

o Your essay should be typed, double-spaced on standard-sized paper (8.5″ x 11″)
o Use 1″ margins on all sides, first line of all paragraphs is indented ½” from the margin
o Use 12 pt. Times New Roman font

 Follow the outline provided above and use section headers to improve the readability of your paper. If I cannot read and understand it,
you will not earn credit for the content.

Critical Elements Proficient (100%) Needs Improvement (70%) Not Evident (0%) Value

Supervised and Unsupervised
learning

Explain the differences
between the two main types of
machine learning methods and
describes the categories

Explain the differences
between the two main types of
machine learning methods but
does not describe the
categories

Does not explain the
differences between the two
main types of machine learning
methods and does not describe
the categories

30

Artificial Neural Nets

Describes how artificial neural
nets (ANNs) use supervised
learning to predict outcomes
and explains how it is used in
forecasting

Describes how artificial neural
nets (ANNs) use supervised
learning to predict outcomes
but does not explain how it is
used in forecasting

Does not describe how
artificial neural nets (ANNs)
use supervised learning to
predict outcomes and does not
explain how it is used in
forecasting

30

Real-world Examples
Provides a real-world example
of each type of learning

Provides a real-world example
of only one type of learning

Does not provide any real-
world examples of each type of
learning

30

Articulation of Response

Submission has no major
errors related to citations,
grammar, spelling, syntax, or
organization.

Submission has major errors
related to citations, grammar,
spelling, syntax, or
organization that negatively
impact readability and
articulation of main ideas.

Submission has critical errors
related to citations, grammar,
spelling, syntax, or
organization that prevent
understanding of ideas.

10

EARNED TOTAL 100%