slide1
Download
Skip this Video
Download Presentation
Lessons from homework

Loading in 2 Seconds...

play fullscreen
1 / 5

Lessons from homework - PowerPoint PPT Presentation


  • 68 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Lessons from homework' - mele


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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?
ad