Assignment:
1. Survey the literature from the past six months to find one application each for DSS, BI, and analytics. Summarize the applications on one page, and submit it with the exact sources.
2. Find information about IBM Watson's activities in the healthcare field. Write a report. (1 page with references)
3. Discuss the difficulties in measuring the intelligence of machines. (150 words with reference)
4. In 2017, McKinsey & Company created a five-part video titled "Ask the AI Experts: What Advice Would You Give to Executives About AI?" View the video and summarize the advice given to the major issues discussed. (150 words with reference)
5. Watch the McKinsey & Company video (3:06 min.) on today's drivers of AI at youtube.com/watch?v=yv0IG1D-OdU and identify the major AI drivers. Write a report. (150 words with reference)
6. Explore the AI-related products and services of Nuance Inc. (nuance.com). Explore the Dragon voice recognition product. (1 page with references)
7. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain. (150 words with reference)
8. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum? (150 words with reference)
9. Where do the data for business analytics come from? What are the sources and the nature of those incoming data? (150 words with reference)
10. What are the most common metrics that make for ¬analytics-ready data? (150 words with reference)
11. Go to data.gov-a U.S. government-sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic-¬specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations. (150 words with reference)
12. Define data mining. Why are there many names and definitions for data mining? (150 words with reference)
13. What are the main reasons for the recent popularity of data mining? (150 words with reference)
14. Discuss what an organization should consider before making a decision to purchase data mining software. (150 words with reference)
15. Distinguish data mining from other analytical tools and techniques. (150 words with reference)
16. Discuss the main data mining methods. What are the fundamental differences among them? (150 words with reference)
17. Visit teradatauniversitynetwork.com. Identify case studies and white papers about data mining. Describe recent developments in the field of data mining and predictive modeling. (150 words with reference)
18. What is an artificial neural network and for what types of problems can it be used? (150 words with reference)
19. Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar? (150 words with reference)
20. What are the most common ANN architectures? For what types of problems can they be used? (150 words with reference)
21. ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode. (150 words with reference)
22. Go to Google Scholar (scholar.google.com). Conduct a search to find two papers written in the last five years that compare and contrast multiple machine-learning methods for a given problem domain. Observe commonalities and differences among their findings and prepare a report to summarize your understanding. (150 words with reference)
23. Go to neuroshell.com. Look at Gee Whiz examples. Comment on the feasibility of achieving the results claimed by the developers of this neural network model. (150 words with reference)
24. What is deep learning? What can deep learning do that traditional machine-learning methods cannot? (150 words with reference)
25. List and briefly explain different learning paradigms/methods in AI. (150 words with reference)
26. What is representation learning, and how does it relate to machine learning and deep learning? (150 words with reference)
27. List and briefly describe the most commonly used ANN activation functions. (150 words with reference)
28. What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN. (150 words with reference)
29. Cognitive computing has become a popular term to define and characterize the extent of the ability of machines/computers to show "intelligent" behavior. Thanks to IBM Watson and its success on Jeopardy!, cognitive computing and cognitive analytics are now part of many real-world intelligent systems. In this exercise, identify at least three application cases where cognitive computing was used to solve complex real-world problems. Summarize your findings in a professionally organized report. (150 words with reference)
30. Explain the relationship among data mining, text mining, and sentiment analysis. (150 words with reference)
31. In your own words, define text mining, and discuss its most popular applications. (150 words with reference)
32. What does it mean to induce structure into text-based data? Discuss the alternative ways of inducing structure into them. (150 words with reference)
33. What is the role of NLP in text mining? Discuss the capabilities and limitations of NLP in the context of text mining. (150 words with reference)
34. Go to teradatauniversitynetwork.com and find the case study named "eBay Analytics." Read the case carefully and extend your understanding of it by searching the Internet for additional information, and answer the case questions. (150 words with reference)
35. Go to kdnuggets.com. Explore the sections on applications as well as software. Find names of at least three additional packages for data mining and text mining. (150 words with reference)