Week 3 Assignment 3 Business Intelligence

 Chapter 5

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.

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.

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

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.

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

Chapter 6 • Deep Learning and Cognitive Computing 385

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