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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

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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

      • 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


This week
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
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
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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