Tasks for Machine Learning and Ensemble Algorithms Replication and Implementation

Keystroke Patterns Classification Using ARTMAP-FD Neural Network

Task:

Q1:The paper (Biggio, Battista, and Fabio Roli. "Wild patterns: Ten years after the rise of adversarial machine learning." Pattern Recognition84 (2018): 317-331.)

Q2: The paper (Loy, C. C., Lai, W. K., & Lim, C. P. (2007, November). Keystroke patterns classification using the ARTMAP-FD neural network. In Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) (Vol. 1, pp. 61-64). IEEE.) included a dataset we mentioned in Week 8
http://personal.ie.cuhk.edu.hk/~ccloy/downloads_keystroke100.html. The paper focuses on two features: latency and pressure. Follow the paper experiments and replicate their results that are summarized in Tables 2 and 3
 
Submit your code and report of how you designed and tested the code to produce the same results

Q4: 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)
Q5: 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)

Q6: 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)
Q7: 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)
Q8 : 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)
Q9: This is a research oriented question on the paper (The security of machine learning)

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)
Q10: 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)
Q11: 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)
Q12: 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)
Q13: 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)
Q14: Implement all Ensemble algorithms described in one of the links
https://github.com/PacktPublishing/Hands-On-Ensemble-Learning-with-R
(one chapter)