7072CEM Assignment Help

MACHINE LEARNING Assignment help

The paper should broadly include the following sections:

• Abstract
• Introduction (where you introduce the problem along a short literature review of related work; if the literature review is longer, it is recommended to be a section on its own, which would be better)
• Problem and Data set(s) description (where you describe in detail the problem you want to solve and its significance)
• Methods (where you shortly describe the machine learning methods and/or other methods employed to solve the problem)
• Experimental setup (including data pre-processing, feature selection and extraction, classification/clustering parameters)
• Results
• Discussion and Conclusions
• References

These are generic section titles, which you may adapt appropriately to the application/problem that is being investigated. You may include sections describing modifications of algorithms or developments that are novel and specific to your work.

You will need to follow the formatting guidelines of the IEEE Manuscript Template for Conference Proceedings (A4)

You may include figures, tables, pseudo-code, and appendices with the actual code that has been developed. You are free to use any programming language you are comfortable with (e.g., Matlab, Python, R, etc.)

More information of how to write a paper is available at the following link: “Crafting Papers on Machine Learning”, by Pat Langley

(which can be found here if the previous link does not work http://www.machinelearning.ru/wiki/images/0/07/Langley00crafting.pdf ).

The group project general guidelines and milestones:

Please note, the following guidelines are good practice and should lead to better result, but you have the freedom to pick whatever is suitable for your style:

• Working in groups of 1, 2 (or 3, not recommended unless you have an extremely complex project which justifies collaboration with more people). If you work in pairs or in groups, you need to inform the lecturer before 25 January 2022 (the first support session).
• You have to select a real-world classification/clustering problem and one or more appropriate dataset(s) as suggested above. You may also use the following links, which have numerous problems and datasets:
• UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/;
• ICML 2019 accepted papers: https://icml.cc/Conferences/2019/Schedule?type=Poster;
• Kaggle competitions: http://www.kaggle.com/competitions;
• Stanford machine learning projects:

http://cs229.stanford.edu/projects2013.html, http://cs229.stanford.edu/projects2012.html, http://cs229.stanford.edu/projects2011.html,

http://cs229.stanford.edu/projects2016.html .

• You do not need to write the proposal about the dataset and machine learning problem.
• In the following weeks until the submission deadline you have to select, implement and apply appropriate machine learning algorithms to the selected problem, performing data pre-processing, if needed, and record the results from the experiments.
• You have to write up your final paper, and submit it by the deadline specified on the first page.