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Whirligig Essay essay help websites Business online class help

In life, we are the ones in charge of ourselves. And in the book Whirligig, people may think that that this lesson does not apply but it does. Look at Brent, he was given the option to not place the whirligigs on the four corners, but he did anyway. And over that trip, Brent changed. Which leads me to believe that every person Is capable of changing no matter their past. First of, In the beginning of the book, Brent was a kid Just worried about fitting In, ND over the course of the book, those menial things don’t matter to him anymore.

Its Like he has grown up and become more mature. For example, his Interests towards the beginning where cars, sports, girls. But In the end of the book, he was more Interested In reading, star gazing, and playing the harmonica, these actively are more for the adult crowd you’d think. So this Just proves that people can change. Second, In the chapter San Diego, with the holocaust survivor, she went through hell.Literally, like the quote says “I don’t know who, said that there should be no laughing after Auschwitz. That nobody could ever want to laugh again after the things that happened here” (peg 113) So she thought she would never feel happiness or laugh again.

But when her granddaughter took her around town, and she saw the whirligig, she changed. Like the quote says ” I glimpsed a smile on my grandmother’s face, and I felt it led” (peg 114). Again proving that people can change, no matter what appended in their past.In all, when you think about it, people can change. No matter what they’ve done, no matter their past. Like with Brent, he was a typical teenager, but in the end, he was acting like a full fledged adult. And the Grandmother in the chapter “San Diego”, she was an elderly holocaust survivor, and she saw unimaginable things.

She thought she would never feel happiness again.

Enterprise Business Intelligence

Enterprise Business Intelligence.

[Description]: This assignment is designed to provide students a good opportunity to read some materials to understand the theory, methods and recent development in the area of business intelligence, especially the modern technologies that are normally used in business intelligence. The typical technologies that are frequently applied in business intelligence come from the data mining and machine learning areas.
In this assignment, you are required to choose one of the following areas, and write a survey on a topic you have chosen in that area:
1. Association rule mining
2. Unsupervised learning
3. Supervised learning
4. Outlier/Anomaly detection
5. Ensemble learning
[Tasks and Requirements]:
Your survey should meet the following requirements:
(1) Should include the following components:
A. A meaningful title followed by your name and student ID
B. An abstract
C. An introduction
D. Body of the Survey
In this section, you need to cite publications/papers that you use, which can include conference proceedings, journal articles, books/book chapters, technical reports or any other publications. You need to clearly state what the paper is about (this could be a theory, an idea, a hypothesis, an assumption, a proposal; it could also be a method, an approach, a technique, an algorithm etc.). How it works (technical details). How it can be used in business intelligence. You can provide comparisons of different methods. Identify the advantages and disadvantages of each method. You could state your idea, your opinion, your view, and/or the other people’s view or opinion about the work/method in the paper.
E. Conclusion/Summary
F. List of Reference ( Please check Deakin Guide to Referencing)
(2) Should be no less than 3000 words and no more than 5000 words (including references)
(3) You are required to read at least 5 references including lecture notes of this unit.
(4) Only references that are used/discussed in your survey should be included in the list of Reference.
Page 1 of 3
[Submission]:
• You must submit your completed document (pdf or word doc) in the Dropbox in
CloudDeakin.
• Remember that late submissions will be penalised. Further, the CloudDeakin server is the
ultimate time keeper when it comes to determining whether your submission has been
received on time.
• You are also reminded to keep a backup copy for record.
[Readings and possible reference]:
All students are required to read at least three articles/papers in the area of the topic you have
chosen. In the following list, we have provided more articles, and the links lead to top international
Journals and conferences in the area. It provides you a good opportunity to know the recent
developments that are happening in those areas.
• Association Rule Mining
• Association Analysis: Basic Concepts and Algorithms
• Mining Association Rules
• Association Rules and AprioRi Algorithm
• Unsupervised Learning
• Shimon Ullman and Tomasa Poggio, Unsupervised Learning.
• Zoubin Ghahramani, Unsupervised Learning
• Wikipedia Definition, Unsupervised learning
• Supervised Learning
• Supervised learning
• What is supervised learning
• What is the difference between supervised learning and unsupervised learning?
Page 2 of 3
• Outlier/Anomaly detection
• Wikipedia Definition: Anomaly detection
• Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Pearson
Higher Ed, 2013.
• Following are two survey papers in this area. In your work, you should also include
new articles that are not used in these survey papers.
o Hodge, V.J. & Austin, A Survey of Outlier Detection Methodologies, J. Artif
Intell Rev (2004) 22: 85. doi:10.1007/s10462-004-4304-y
o Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly
detection: A survey. ACM Comput. Surv. 41, 3, Article 15 (July 2009), 58
pages. DOI=http://dl.acm.org/citation.cfm?id=1541882
• Ensemble Learning
• Wikipedia Definition, Ensemble learning
• Thomas G Dietterich, Ensemble Methods in Machine Learning
• Bagging, boosting and stacking in machine learning.
• Ensemble Learning.
International Conferences
1. ICML – International Conference on Machine Learning, ICML2017, ICML 2016, ICML
2015
2. IJCAI – International Joint Conference on Artificial Intelligence, IJCAI2017, IJCAI 2016,
IJCAI 2015
3. ICDM – International Conference on Data Mining,ICDM2017, ICDM 2016, ICDM2015
4. KDD – ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining,KDD2017, KDD2016, KDD2015
5. Business Intelligence Conference
6. International Conference on Business Intelligence and Technology, BUSTECH2017,
BUSTECH 2016
7. International Conference on Business Intelligence and Data Mining,ICBIDM2017, ICBIDM
2016, ICBIDM 2015
International Journals
1. Top journals in data mining
2. Top journals in machine learning & pattern recognition

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