Description

Considering the following problems:
(i) Building a system that guesses what the weather (temperature, precipitation, etc.) will be like tomorrow
(ii) Predicting products that a customer would be interested in buying, based on other purchases that customer has previously made
(iii) Skin cancer screening test
(iv) Automatically identifying the author of a given piece of literature
(v) Finding the best burrito in the United States of America
1. Identify the “concept” we might attempt to “learn” for each problem (Task Identification)
2. For each problem-task, identify what the “instances” and “attributes” might consist of (Choosing the Data Representative)
3. For each problem-task, conjecture whether a typical strategy is likely to use “supervised” or “unsupervised” Machine Learning (Picking a Suitable Model)
4. For each problem-task, consider how easy or difficult it would be to make a model that generalizes to new cases. For example, could you predict the weather in any city in the world, or just in one specific city?
5. What kinds of assumptions might a machine learning model make when tackling these problems?