Decision-making with Technology

 

Topic:  Decision-making with Technology

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.

Please ensure you read the assignment rubric for specific details on the requirements for this essay!

Topic:Essay on Decision making with technology

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.

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?

AI. 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?

BI. 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:

· Your essay should be typed, double-spaced on standard-sized paper (8.5″ x 11″)

· Use 1″ margins on all sides, first line of all paragraphs is indented ½” from the margin

· 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.

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Critical Elements	Proficient ( 10 0%)	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
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
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%

Data mining and the General Data Protection Regulation (GDPR):

Data mining is a complex subject dominated by emerging technologies and privacy regulations, and consumers gained better control over their personal data when the General Data Protection Regulation became enforceable on May 25, 2018. Under GDPR, profiling is determined to be any kind of automated personal data processing that analyzes or predicts certain aspects of an individual’s behavior, socioeconomic situation, movements, preferences, health and so forth.

Describe

 

two major impacts that GDPR has on the process and practice of data mining.

Respond substantively to at least two other students’ posts. Comment on how GDPR has changed the way in which every business stores, processes, transfers, and analyzes its data based on the impacts discussed in your classmate’s initial post.

*please remember to include at least one credible scholarly reference!