CSC 151.01, Class 31: Project introduction

Overview

• Preliminaries
  • Notes and news
  • Upcoming work
  • Extra credit
  • Quiz 10
  • Questions
• Quiz
• About the project
• Group composition and construction
• Analysis lab
• Debrief (?)

News / Etc.

• Welcome to our guest!
• Please try to keep the room straight.
• Quiz 10 returned.

Upcoming Work

• Reading for Monday: Association lists
  • Yes, it’s ready!
• Writeup for class 31 due Monday at 10:30 p.m.
  • Exercise TBD
  • To: csc151-01-grader@grinnell.edu
  • Subject: CSC 151.01 Writeup 31 (YOUR NAMES)
• Exam 3
  • Prologue due TONIGHT
  • Exam due Tuesday the 14th.
  • Cover pages due Wednesday the 15th.
  • Epilogues due Wednesday the 15th.

Extra credit (Academic/Artistic)

Extra credit (Peer)

• Voice Recital (students of Nicolas Miguel), TONIGHT, 7:30 p.m., Sebring-Lewis
• Eat at ISO food bazaar.
• Pioneer weekend walkins at 6pm today.

Extra credit (Misc)

Other good things

• Showvember tonight

Quiz 10

Notes

• Did not go well.
• So we’ll go over it.
• I’m now dropping the two lowest quiz grades in calculating your final grade.

Problem 1

(cons
  (cons 5 10)
  (list 15
        20
        (cons 25 null)))

See whiteboard for drawing.

'((5 . 10) 15 20 (25))

Problem 2: Find smallest in a vector.

• We’re going to need a helper to keep track of position and smallest-so-far. We can use named let, letrec, or a separate procedure.
• By popular demand, we use named let.

(define smallest-in-vector
  (lambda (vec)
     (let kernel ([pos 0] 
                  [smallest-so-far (vector-ref vec 0)])
       (if (>= pos (vector-length vec))
           smallest-so-far
           (kernel (+ 1 pos) (min smallest-so-far (vector-ref vec pos)))))))

Questions exam

Question in prep for next quiz

Quiz

About the project

• Do something interesting of your choice within constraints
  • New and different data set
  • Requires some processing/munging/whatever you want to call
  • With a novel algorithm that does something to help you understand the data
• Literature classifier
• Text generation
• Cancer cell identification (or something similar)
• A set of tools for dealing with a data set.
• For a week from Monday: Find the data set and Write a proposal
  • Describe set
  • Describe your intended goals with the data set
    • What you think you can do
    • What would satisfice
    • Reach goal
  • Describe the algorithms you will be generating
• Time-boxed: 8-10 hours per person over two+ weeks
• Ideal group size: 3 (or 2 or 4 or 1)

Group composition and construction

• A creative person so that we don’t do a really boring project.
• Big picture person
• Someone who can turn the big picture into a working program (“driver”)
• Someone who can write
• Someone who can make decisions so that we don’t sit there debating endlessly.
• Someone who can plan - make sure people are there (or contribute).
• Positve personality to help us through the hard times.

Sam chooses four

• Big picture
• Coder/driver
• Writer
• Leader/planner/coordinator/manager

Lab

Debrief