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(ii) Print preview of ipynb file named as StudentID-Name-HW1.pdf (with all results)
(iii) Working ipynb file named as StudentID-Name-HW1.ipynb
1. A machine learning application scenario can be seen from four perspectives: (i) what is the technical problem to be solved, (ii) the data requirements, meaning can you get the data needed, (iii) security and privacy considerations, meaning what happens if data is leaked and (iv) the value proposition that machine learning brings to the table.
Choose two industries from the list below (or be creative and come up with your own) and give one example from each industry of how supervised machine learning can be applied. Your examples should follow the 4 perspectives outlined above.
Retail Fashion Industry 4.0
Banking Education Social Media
Healthcare Communication Networks Smart Home
2. Redo Problem 1 for the unsupervised learning scenario. Give one example of an application scenario that is different from the examples discussed in the lectures.
3. Redo Problem 1 for the reinforcement learning scenario. Give one example of an application scenario that is different from the examples discussed in the lectures.
4. Suppose we want to remove vowels from a sentence. Write Python code to do this using iterators and list comprehension. The input sentence is: "The quick brown fox jumps over the lazy dog".
a. Paste you Python code below, as well as the output of your program with the given input.
b. Submit your iPython notebook file (ipynb file) as well as a pdf print preview of the ipynb file as instructed above.
