1. Please read both articles on IBM and Oracle AI in the Readings Thread

1. Please read both articles on IBM and Oracle AI in the Readings Thread

2. In 3-4 sentences, (in your own words, not copied from the article) describe how IBM presents AI.

3. In 3-4 sentences, (in your own words, not copied from the article) describe how Oracle presents AI.

4. What are the differences and similarities of each companies presentation of AI?

5. In your opinion, and why, which one do you think is the best presentation/introduction of AI?

Module 2 DQ #2 AI & Critical Decision Making

1. Complete your text readings

2. In 3-4 sentences describe how AI can be used as a tool for business, science, and engineering decision making.

3. How can AI help with data visualizations (3-4 sentences)

4. In your opinion, what are some downfalls or downsides of using AI in critical decision making, for example in medicine?

MSCS2201

module 2-Assignment 2

100 points

Define the following terms. For each, use your own words. Describe each in the context of our textbook. (20 points)
Artificial intelligence
Business intelligence
Predictive analytics
Prescriptive analytics
Machine Learning
Deep Learning
Natural Language Processing
What is meant by analytics? Give several examples of specific types of analytics. (5 points)
What is the relationship between statistics and business analytics? (5 points)
What is meant by data visualization? Give several examples of data visualization techniques.(5 points)
Define intelligent agents and list some of their capabilities. (5 points)
What is regression, and what statistical purpose does it serve? (5 points)
What are the commonalities and differences between regression and correlation? (5 points)
What does it mean to clean/scrub the data? What activities are performed in this phase?(5 points)
Go to https://catalog.data.gov/dataset?res_format=CSVLinks to an external site. (30 points)Review the available datasets. Find one dataset of CSV data with a large number of entries. Include the csv with your submission. Open the CSV in Excel to review the data. Answer the following about your data. Hint: If there isn’t much data in the file, it will not be interesting to review.
What is the topic of this data?
What does each record represent?
Give several questions that could be answered with this data. Be specific.
See Application Case 3.1 from the textbook. (15 points)“Verizon Answers the Call for Innovation: The Nation’s Largest Network Provider uses Advanced Analytics to Bring the Future to its Customers”Answer the “Questions for Case 3.1”
What was the challenge Verizon was facing?
What was the data-driven solution proposed for Verizon’s business units?
What were the results
MSCS2201Module3
Assignment 2 100 points
Define the following terms. For each, use your own words.
Describe each in the context of our textbook. (30 points)
k-nearest neighbor KNN
Classification
Regression
Corpus
Data Mining
Web Analyitics
Natural Language Processing
What is a support vector machine?
Why are support vector machines a popular machine-learning technique?How can this be done using R?(5 points)
What is natural language processing?
Describe the specific techniques used.
List and describe several of the specific challenges of NLP.
(5 points)
How does sentiment analysis relate to text mining?
What are the common challenges with which sentiment analysis deals?What are the most popular application areas for sentiment analysis? Why?(5 points)
Describe how sentiment analysis is done.
Give the steps required to do this in R. Be specific.
(5 points)
What is a correlation? How is correlation done in R?
Give an example and explain the meaning for the results in R.
(5 points)
What is a confusion matrix?
Give an example of a confusion matrix, and explain the meaning.
(5 points)
Most of the applications of deep learning today are developed using R- and/or Python-based open-source computing resources. Identify those resources (frameworks such as Torch, Caffe, TensorFlow, Theano, Keras) available for building deep learning models and applications. Compare and contrast their capabilities and limitations. Based on your findings and understanding of these resources, if you were to develop a deep learning application, which one would you choose to employ? Explain and justify/defend your choice.
(10 points)
Consider 3 different sources for text data to use for continual analysis. Describe how this data could be obtained, processed, and what could it be used for.
Do not complete the analysis.
Example:
– Use Python Twitter API to search for new Tweets about, but not authored by, Elon Musk.
– Remove, strip, process the text
– Using sentiment analysis, determine if Elon is being a Jerk today.
– Calculate and post the ‘Daily Elon Threat Level’ on a website for the Tesla employees break room. (10 points)
Go to https://www.crummy.com/software/BeautifulSoup/bs4/doc/
– Review the documentation for the Beautiful Soup library for Python. – Complete the Quick Start Guide examples. Document this work.
Then answer the following:
How can this be useful for machine learning and AI?
List 3 websites that could be useful for data/text mining with BeautifulSoup.
For each, give the URL and a specific feature to look for.
After using BS to get the data, how would you process it?
(20 points)V