Analytics Data Science And Artificial Intelligence Systems For Decision Support 11th Global Edition Ramesh Sharda, Dursun Delen, Efraim Turban - Solutions

Describe the components of the time/place framework.
List process gains made by GDSS.
How is CI supported by computers?
List the major benefits of the technology.
How can AI facilitate idea generation?
Discuss the use of teams of robots.
Explain in what ways physical meetings can be inefficient. Explain how technology can make meetings more effective.
What is Slack, and how it is used? How is it different from other online cooperation tools?
List the major activities that occur in group work.
Describe the importance of collaboration for decision making.
Describe collaborative workflow.
How can CI change work and life?
List some areas for which crowdsourcing is suitable.
What is the analogy of swarm AI to swarms of living species?
What will do robots on Mars?
The initial term for group support system (GSS) was group decision support system (GDSS). Why was the word decision dropped? Does this make sense? Why, or why not?
Some software tools are aimed at improving cooperative work for designers. Do some research on the Internet and compare two of them.
List and discuss five benefits of group work.
What is collaborative workspace? What are its benefits?
Describe Web-based GSS.
How can CI impact organization structure and decision making?
Why may you need a vendor to crowdsource the problem-solving process?
How is swarm AI used to improve group work and to initiate group predictions?
Discuss why Microsoft SharePoint is considered a workspace. What kind of collaboration does it support?
How does the creative business sector use fanbases to support creative endeavors?
List and discuss five dysfunctions of group-made decisions.
Describe social collaboration.
Describe how GDSS supports brainstorming and idea generation.
What are SVM? How do they work?
Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
What are the most common metrics that make for analytics-ready data?
What is an artificial neural network and for what types of problems can it be used?
Go to the Teradata University Network Web site (teradatauniversitynetwork.com) or a URL given by your instructor. Locate Web seminars related to data mining and neural networks. Specifically, view the seminar given by Professor Hugh Watson at the SPIRIT2005 conference at Oklahoma State University;
Explore the Web sites of several neural network vendors, such as California Scientific Software (calsci.com), NeuralWare (neuralware.com), and Ward Systems Group (wardsystems.comv), and review some of their products. Download at least two demos and install, run, and compare them.
Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar?
A very good repository of data that have been used to test the performance of neural network and other machine-learning algorithms can be accessed at https://archive.ics.uci.edu/ml/index.php. Some of the data sets are really meant to test the limits of current machine-learning algorithms and
What are the most common ANN architectures? For what types of problems can they be used?
Download the report “Three Advanced Analytics Solution Providers in Asia/Pacific Named IDC Innovators” at https://www.idc.com and discuss the latest trends in neural networks in Singapore and the rest of the Asia Pacific region.
Go to Linker Networks’ Web site (http://www.linkernetworks. com/) and discuss the use of neural networks to teach vehicles how to accurately recognize objects around them through its auto-labeling system.
Go to nd.com. Download the trial version of NeuroSolutions for Excel and experiment with it using one of the data sets from the exercises in this chapter. Prepare a report about your experience with the tool.
What are the types of problems that can be solved by SVM?
Machine learning is considered the best approach to solving complex business problems involving a large amount of data and lots of variables. Visit the MATLAB for Machine Learning site at https://uk.mathworks.com/solutions/machine-learning.html?s_tid=hp_brand_machine and discuss the different
Explore Linker Networks’ Web site (http://www.linkernetworks. com/). What can its auto-labelling system be used for besides training vehicles? Read the featured stories on the Web site and name at least two.
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.
What is the kernel trick and how does it relate to SVM?
Go to easynn.com. Download the trial version of the software. After the installation of the software, find the sample file called Houseprices.tvq. Retrain the neural network and test the model by supplying some data. Prepare a report about your experience with this software.
What are the specific steps to follow in developing an SVM model?
Visit tibco.com. Download at least three white papers of applications. Which of these applications might have used neural networks?
How can the optimal kernel type and kernel parameters be determined?
Visit OpenNN (http://www.opennn.net), a free neural network library for advanced analytics, and discuss how its algorithms relate to real-world applications in energy, marketing, health, etc.
Visit Pytorch (http://pytorch.org/), which uses Python to speed up training and testing in some deep neural network models. Discuss a few of the examples provided.
What are the commonalities and differences, advantaged and disadvantages between ANN and SVM?
Go to sas.com. Download at least two customer success stories or case studies that use advanced analytics or machine learning. Prepare a presentation for your understanding of these application cases.
Explain the difference between a training and a testing data set in ANN and SVM. Why do we need to differentiate them? Can the same set be used for both purposes? Why or why not?
Go to teradata.com. Download at least two customer success stories or case studies where advanced analytics or machine learning is used. Prepare a presentation for your understanding of these application cases.
Everyone would like to make a great deal of money on the stock market. Only a few are very successful. Why is using an SVM or ANN a promising approach? What can they do that other decision support technologies cannot do? How could SVM or ANN fail?
What are the common (business and scientific) applications of kNN? Conduct a Web search to find three realworld applications that use kNN to solve the problem.
What type of data can be used in a Naïve Bayes algorithm? What type of predictions can be obtained from it?
What is a model ensemble, and analytically where can it be used?
Go to the Teradata University Network Web site (teradatauniversitynetwork.com). Search for teaching and learning materials (e.g., articles, application cases, white papers, videos, exercises) on deep learning, cognitive computing, and IBM Watson. Read the material you have found. If needed, also
What is deep learning? What can deep learning do?
How does a single artificial neuron (i.e., PE) work?
Despite the short tenure of deep learning implementation, why do you think there are several different computing frameworks for it?
What is cognitive computing, and how does it differ from other computing paradigms?
Football. Soccer. The beautiful game. Whatever you call it, the world's most popular sport is being transformed by a Dutch start-up bringing AI to the pitch.SciSports, founded in 2012 by two self-proclaimed football addicts and data geeks, is innovating on the edge of what is possible. The sports
What is deep learning? What can deep learning do that traditional machine-learning methods cannot?
Compare the supercomputers Sunway TaihuLight (China) and Summit (USA). Write a report on your findings.
Compared to traditional machine learning, what is the most prominent difference of deep learning?
List and briefly describe the most commonly used ANN activation functions.
What is GPU? How does it relate to deep neural networks?
For what type of applications can CNN be used?
Video gamers are a special breed. Sure, they spend a lot of time playing games, but they're also building social networks. Like sports athletes, video game players thrive on competition. They play against other gamers online. Those who earn first place, or even second or third place, have bragging
What is the significance of “context,” “sequence,” and “memory” in RNN?
Define CPU, NVIDIA, CUDA, and deep learning, and comment on the relationship between them.
Draw a diagram and explain the conceptual framework of cognitive computing. Make sure to include inputs, enablers, and expected outcomes in your framework.
List and briefly explain different learning paradigms/ methods in AI.
Discuss the latest discoveries in quantum computing, including Google’s Fall 2019 announcement of a breakthrough in this area.
List and briefly explain different learning methods in AI.
What is MLP, and how does it work?
Draw and briefly explain the three-step process of learning in ANN.
How does sensitivity analysis work in ANN?
How does a feedforward multilayer perceptron-type deep network work?
What is convolution function in CNN and how does it work?
Draw and explain the functioning of a typical recurrent neural network unit.
List and briefly define the characteristics of different deep learning frameworks.
Explain the function of weights in ANN.
"There are some people who want to kill animals like the lions and cheetahs. We would like to teach them, there are not many left," says WildTrack officials.The more we can study their behavior, the more we can help to protect them-and sustain the earth's biodiversity that supports us all. Their
List and briefly define the key attributes of cognitive computing.
What is representation learning, and how does it relate to machine learning and deep learning?
Deep learning has strong potential for use in the space industry. Research some of the possible applications of AI in the space industry on the Internet.
What is representation learning, and how does it relate to deep learning?
Face recognition, although seemingly similar to image recognition, is a much more complicated undertaking.The goal of face recognition is to identify the individual as opposed to the class it belongs to (human), and this identification task needs to be performed on a nonstatic (i.e., moving person)
When the Georgia Department of Transportation (GDOT) wanted to optimize the use of Big Data and advanced analytics to gain insight into transportation, it worked with Teradata to develop a proof of concept evaluation of GDOT's variable speed limit (VSL) pilot project.The VSL concept has been
Comment on the impact of random weights in developing deep MLP.
What is the LSTM network, and how does it differ from RNNs?
Search the Internet to find other methods to explain ANN methods. Report the results.
How does cognitive computing differ from ordinary AI techniques?
List and briefly describe the most commonly used ANN activation functions.
What is pooling in CNN? How does it work?
What is Keras, and how is it different from the other frameworks?

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Analytics Data Science And Artificial Intelligence Systems For Decision Support 11th Global Edition Ramesh Sharda, Dursun Delen, Efraim Turban - Solutions

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