Lessons from homework Try the simplest thing first

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 • Rule learning – Reading: Mitchell, Chapter 10 • Evaluating hypotheses – Reading: Mitchell, Chapter 5 • Homework #2 assigned later today – Due 5: 00 PM 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?