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Lessons from homework

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

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Lessons from homework

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  1. 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

  2. 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

  3. 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

  4. 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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