· With the exception of research questions, all questions should be answered using the dataset that you have selected for the final, or they will not be graded. 

· Important: 

· With each one of those non-theoretical questions deliver two parts in two separate files: ((1) a document file or code report of code design and testing and (2) the code itself should be submitted). 

· If you have questions, you can always email me for clarifications.

Q1: Read the paper (Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia Cirp, 38, 3-7.) Going to Google Scholar, you can see that the paper is cited 155 times so far. 

Industrial big data analytics and cyber-physical systems for future maintenance & service innovation

J Lee, HD Ardakani, S Yang, B Bagheri – Procedia Cirp, 2015 – Elsevier

With the rapid advancement of Information and Communication Technologies (ICT) and the
integration of advanced analytics into manufacturing, products and services, many industries
are facing new opportunities and at the same time challenges of maintaining their …

Cited by 155 Related articles All 4 versions

Pick the original paper, and (3-5) of the papers that cited this original paper (from the 155 listed in Google scholar). Then read and summarize the papers in your own words (size 2-4 pages double line 12 fonts times new roman). Your own words mean write your own sentences for the original paper and also the cited papers. You need to summarize properly.

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Q2: Read the paper/patent (Amini, L., Christodorescu, M., Cohen, M. A., Parthasarathy, S., Rao, J., Sailer, R., … & Verscheure, O. (2015). U.S. Patent No. 9,032,521. Washington, DC: U.S. Patent and Trademark Office.)

Adaptive cyber-security analytics

L Amini, M Christodorescu, MA Cohen… – US Patent …, 2015 – Google Patents

Performing adaptive cyber-security analytics including a computer implemented method that
includes receiving a report on a network activity. A score responsive to the network activity
and to a scoring model is computed at a computer. The score indicates a likelihood of a …

Cited by 78 Related articles All 4 versions 

Pick the original paper/patent, and (3-5) of the papers that cited this original paper/patent (from the 78 listed in Google scholar). Then read and summarize the papers in your own words (size 2-4 pages double line 12 fonts times new roman). Your own words mean write your own sentences for the original paper and also the cited papers. You need to summarize properly.

Q3: (Apply this on your selected code, submit two files: code/report) 

Implement all Ensemble algorithms described in one of the links:

An Intro to Ensemble Learning in R

https://www.r-bloggers.com/an-intro-to-ensemble-learning-in-r/

Submit (your own code + a document to explain how you designed/tested your code)

Q4: (Apply this on your selected code, submit two files: code/report)

Implement all Ensemble algorithms described in one of the links:

How to build Ensemble Models in machine learning? (with code in R)

https://www.analyticsvidhya.com/blog/2017/02/introduction-to-ensembling-along-with-implementation-in-r/

Submit (your own code + a document to explain how you designed/tested your code)

Q5: (Apply this on your selected code, submit two files: code/report)

Implement all Ensemble algorithms described in one of the links:

How to Build an Ensemble Of Machine Learning Algorithms in R

https://machinelearningmastery.com/machine-learning-ensembles-with-r/

Submit (your own code + a document to explain how you designed/tested your code)

Q6: (Apply this on your selected code, submit two files: code/report)

Implement all Ensemble algorithms described in one of the links:

Code for Workshop: Introduction to Machine Learning with R

https://shirinsplayground.netlify.com/2018/06/intro_to_ml_workshop_heidelberg/

Submit (your own code + a document to explain how you designed/tested your code)

Q7: (Apply this on your selected code, submit two files: code/report)

Implement all Ensemble algorithms described in one of the links

Machine Learning With R: Building Text Classifiers

https://www.springboard.com/blog/machine-learning-with-r/

Submit (your own code + a document to explain how you designed/tested your code)

Q8: This is a research oriented question on the paper (The security of machine learning)

Barreno, M., Nelson, B., Joseph, A. D., & Tygar, J. D. (2010). The security of machine learning. Machine Learning, 81(2), 121-148.

Pick the original paper, and (3-5) of the papers that cited this original paper (from the 374 listed in Google scholar). Then read and summarize the papers in your own words (size 2-4 pages double line 12 fonts times new roman)

Q9: This is a research oriented question on the paper (Combining ensemble of classifiers by using genetic programming for cyber security applications)

Folino, G., & Pisani, F. S. (2015, April). Combining ensemble of classifiers by using genetic programming for cyber security applications. In European Conference on the Applications of Evolutionary Computation (pp. 54-66). Springer, Cham.

Pick the original paper, and (3-5) of the papers that cited this original paper (from the 18 listed in Google scholar). Then read and summarize the papers in your own words (size 2-4 pages double line 12 fonts times new roman)

Q10: (Apply this on your selected code, submit two files: code/report)

Implement all Ensemble algorithms described in one of the links

Machine Learning and NLP using R: Topic Modeling and Music Classification

https://www.datacamp.com/community/tutorials/ML-NLP-lyric-analysis

Submit (your own code + a document to explain how you designed/tested your code)

Q11: Implement all Ensemble algorithms described in one of the links

Lyric Analysis with NLP & Machine Learning with R

https://www.datacamp.com/community/tutorials/R-nlp-machine-learning

Submit (your own code + a document to explain how you designed/tested your code)

Q12: (Apply this on your selected code, submit two files: code/report)

Implement all Ensemble algorithms described in one of the links

Tidy Text Mining with R

https://github.com/dgrtwo/tidy-text-mining

Submit (your own code + a document to explain how you designed/tested your code)

Q13: (Apply this on your selected code, submit two files: code/report)

Implement all Ensemble algorithms described in one of the links

https://github.com/PacktPublishing/Hands-On-Ensemble-Learning-with-R 

(one chapter)

Submit (your own code + a document to explain how you designed/tested your code)