Multiple Hypothesis Learning - Data Science
In this video, we learn how a computer learns from data. We also learn about the problem that comes from the computer learning from data: the data we train on becomes less useful for understanding validation.
Link to my notes on Introduction to Data Science: https://github.com/knathanieltucker/d...
Try answering these comprehension questions to further grill in the concepts covered in this video:
1. If only we could figure out what the difference between the error on the sample and the population is? If you had access to the full population could you figure it out? What comes next?
2. Why do we want to offload learning to a computer?
3. What is the human’s part in all of this?
4. Can computers learn better than humans?
5. What happens in the learning algorithm? Why is it important?
6. We learned the chance of a rare event occurring increases as the number of events increases. Why is that? Is this always true?
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