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CS801 Quantitative Methods in AI
Task:
Aim of the Assignment:
Your task in this assignment is to look at three different Kaggle notebooks and reflect on the task(s) that the authors are attempting to address with Python and some related AI/ML libraries. In particular, you should comment on steps within the process of setting up and executing these notebooks that require some use or understanding of the quantitative methods (approaches from mathematics) that have formed the focus of the CS801 module.
Learning Outcomes:
After completing this assignment, you should better appreciate how knowledge of the relevant maths can aid in your understanding and critique of various AI/ML projects (almost all of which rely on a variety of quantitative methods within the algorithms used). In the process of carrying out this exercise you will also become more familiar with Kaggle which is an important ML platform, as well as a useful source of data and shared code.
Tasks:
Some background notes on Kaggle and ways to explore projects on that platform are given in the notes that follow. Based on the material covered in class, as well as your own reading, you should, for each project/notebook, comment on the following:
(i) Outline the primary objective of each Kaggle project (sometimes this may not be entirely clear even after reading the description). The notebooks/projects identified have not been designed to be used as ‘tutorials’ and as such may jump straight into analyses. You should summarise the overall objective(s) and also provide some comments as to whether you feel that the project/notebook has adequately addressed (begun to address) this/these objective(s).
(ii) At specific points in the process of working through each notebook, point out where knowledge of a relevant quantitative method might be important (e.g. this could be something as simple as understanding that knowing the statistical distribution which underlies a variable is important to the description of all but the most trivial of data, or that to create training/test sets some form of random sampling – perhaps carried out in a ‘stratified’ manner – will be required to create valid data sets). You should provide comments / annotations within your own copy of each notebook, as well as providing overall commentary in a separate PDF document. Please make sure that within the PDF you indicate clearly which cells(s) within the notebook each of your comments relate to (remembering that the Jupyter cell references change ‘dynamically’).
(iii) Comment on specific parts of the process that you feel were not handling particularly well and feel free to illustrate your criticism with alternative output in the notebook. However, remember that this is primarily meant to be a critique of an existing piece of work, not a total re-implementation of someone else’s code (!) – so don’t spend an excessive amount of time on extra coding tasks.
CS801 Quantitative Methods in AI
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