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Lessons from homework

- Try the simplest thing first
- “Occam’s Razor”: Prefer the simplest hypothesis that fits the data
- Corresponds to the decision tree bias
- Shown to be useful empirically (various mostly unsatisfying philosophical justifications also exist)

- “Laziness” rule
- If it works, you’re done

- “Follow the data” rule
- If it doesn’t work, you learn how to proceed

- “Justify yourself” rule
- Your audience/boss/customer will resist a complex model unless you’ve shown simple ones are inadequate

- “Occam’s Razor”: Prefer the simplest hypothesis that fits the data

This week

- Rule learning
- Reading: Mitchell, Chapter 10

- Evaluating hypotheses
- Reading: Mitchell, Chapter 5

- Homework #2 assigned later today
- Due 5:00PM October 23
- Shorter than last time

Project Grading

- Questions
- How did you encode your task? Why is this reasonable?
- Which ML approaches? Why?
- How did you evaluate your system?
- Were you successful? Why or why not? What did/would you try next?

- Grading based on:
- Thoroughness of evaluation
- Understanding of ML issues (e.g. overfitting, inductive bias, etc.)
- Quality of presentation
- Not on ultimate performance of your system

How to formulate an ML task

- Example: Web pages
- Classify as Student, Instructor, Course
- What are the input features?
- Would you use DTs or NNs?

- Example: Face Recognition
- Identify as one of 20 people
- What are the input features?
- DTs or NNs?

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