Chapter Discussions
Initial post (goal: by Weds)
+ 2 responses (100-300 words).
Assignments
Choose any 2 questions each week: Must provide a
heading to identify which questions were chosen.
Must be a WORD document with APA formatting.
Due
Date
CH #1 Compare and contrast predictive
analytics with prescriptive and
descriptive analytics. Use examples.
Discussion #1 (Applications)
Exercises #5 (analytics-magazine.org)
Exercise #15 (Watson in healthcare)
08/30
CH #2 Discuss the process that generates
the power of AI and discuss the
differences between machine
learning and deep learning.
Discussion #1 (Measuring machine intelligence)
Exercise #4 (5-part video)
Exercise #5 (today’s drivers of AI)
Exercise #15 (nuance.com)
09/06
CH #3 Why are the original/raw data not
readily usable by analytics tasks?
What are the main data pre-
processing steps? List and explain
their importance in analytics.
Discussion #1 (Analytics w/o data)
Discussion #2 (inputs & outputs)
Discussion #3 (data sources)
Discussion #4 (metrics)
Exercise #12 (data.gov)
09/13
CH #4 What are the privacy issues with
data mining? Do you think they are
substantiated?
Discussion #1 (names & definitions)
Discussion #2 (recent popularity)
Discussion #3 (purchasing software)
Discussion #4 (distinguish data mining)
Discussion #5 (data mining methods)
Exercise #2 (teradata seminars)
09/20
CH #5 What is the relationship between
Naïve Bayes and Bayesian
networks? What is the process of
developing a Bayesian networks
model?
Discussion #1 (ANN problems)
Discussion #2 (artificial vs biological NN)
Discussion #3 (ANN architectures)
Discussion #4 (supervised vs unsupervised)
Exercise 6 (scholar google machine-learning)
Internet Exercise #7 (neuroshell.com)
09/27
CH #6 List and briefly describe the nine-
step process in conducting a neural
network project.
Discussion #1 (what is deep learning)
Discussion #2 (learning paradigms/methods)
Discussion #3 (representation learning)
Discussion #4 (common ANN activation functions)
Discussion #5 (what is MLP)
Exercise #4 (cognitive computing cases)
10/04
CH #7 What are the common challenges
with which sentiment analysis deals?
What are the most popular
application areas for sentiment
analysis? Why?
Discussion #1 (data vs text mining vs sentiment-a)
Discussion #2 (define text mining)
Discussion #3 (induce structure – text-based data)
Discussion #4 (role of NLP)