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Well-posed Machine Learning problems
(a) What is required to de ne a well-posed learning problem ?
(b) Here are two potential real-world application tasks for machine learning:
1. a winery wishes to uncover relationships between records of the quantitative analyses of its wines from the lab and some key subjective descriptions applied to its wine (e.g. dry, fruity, light, etc.)
2. you want to predict students' marks in the nal exam of COMP9417 given their marks from the other assessable components in the course | you may assume that the corresponding data from previous years is available
Pick one of the tasks and state how you would de ne it as a well-posed machine learning problem in terms of the above requirements.
(c) Suggest a learning algorithm for the problem you chose (give the name, and in a sentence explain why it would be a good choice).
What advantages does first one have over the second and what advantages does the second have over the first? What would be your criteria for choosing one over the other for different applications.
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