What is an artificial neural network and for what types of problems can it be used? 2. Compare artificial and biological neural networks. What aspects of bi

Week 3 assignment: Complete the following assignment in "one MS Word document": 

Textbook:

Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support Dursun Delen 

Chapter 5:

discussion questions #1-4 (100 – 120 words each answer)

1. What is an artificial neural network and for what types of problems can it be used?

2. Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar?

3. What are the most common ANN architectures? For what types of problems can they be used?

4. ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode.

exercise #6 : (1 page)

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.

internet exercise #7 (1 page)

(go to  neuroshell.com click on the examples and look at the current examples.  The Gee Whiz example is no longer on the page.) 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.

Chapter 6:

discussion question #1-5 (100 – 120 words each answer)

1. What is deep learning? What can deep learning do that traditional machine-learning methods cannot?

2. List and briefly explain different learning paradigms/methods in AI.

3. What is representation learning, and how does it relate to machine learning and deep learning?

4. List and briefly describe the most commonly used ANN activation functions.

5. What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN.

exercise #4 (1 page)

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 realworld 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.

Note: When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.

All work must be original (not copied from any source).

within 8hrs, with references, APA format, plagiarism check required