An odyssey through computer science

Textbook: Chapters 1 and 2.

Our goal

What's this all about?

To understand the

• scope,
• techniques, and
• contributions

People

Dr. Bob Frederking (and the ghost of CBurch)
Email: ref@cs.cmu.edu

Giancarlo Dozzi
Raksha Kumar
Anthony Velázquez

GOVIES

Assignments

• Due MONDAYS
• Assigned and turned in via class webpages.
• Start early!
• Collaborate! (But credit your collaborators.)
• Use cluster hours!
• Assignment 0 is already available.

Lecture

• Lecture notes on class webpages
• Ask questions!

Textbook

``The textbook is excellent! It is very easy to understand and I used constantly as a reference while programming.''

``You could also assign pages to read in the textbook so that the students are somewhat prepared and not as clueless.''

You asked for it...

Webpage

• www.cs.cmu.edu/~ref/pgss
• Register using the ticket you received today.

Self-Study

For those with programming experience:

• Go to computer clusters for sessions 2-7(?).
• Work at own pace.
• TAs will be there.

Definition

Enough preliminaries! What is computer science?

Computer science seeks to answer:

Computers

But computer science should be about computers!

Computing science would be a better name. (Or maybe ``Informatics''?)

But computers are essentially tools for problem-solving:

• fast
• reliable (usually)
• correct (hopefully)

CS circles

Let's look at the ``circles'' of CS.

• Theory
• Systems
• Practice
• Artificial intelligence
• Programming languages

First circle: Theory

What are the fastest ways to solve particular problems?

• Mathematical approach
• Looks for
  • fast methods (algorithms)
  • lower bounds (complexity)
• Overlaps with discrete math

Second circle: Systems

How can we build better problem-solving tools?

• Emphasis on engineering
• Examines
  • Computers (hardware engineering)
  • Software (software engineering)
• Overlaps with electrical engineering

Third circle: Practice

How can we build better programs?

• Looks for
  • reliability and cost-effectiveness (software engineering)
  • usability (human-computer interaction)
• Overlaps with social sciences, industrial design

Fourth circle: Artificial intelligence

How can we behave `intelligently' automatically?

• Facets:
  • Reasoning: learning, understanding, planning
  • Perception: vision, language technologies
  • Action: robotics, language technologies
  • Computational Biology??
• Overlaps with psychology, linguistics, mechanics, control theory

Fifth circle: Programming languages

How can we best represent procedure?

• Looks for how to incorporate new features into programming languages:
  • security
  • parallelism
  • other features
• Overlaps with logic

The plan

We're covering all that?

Table 1: Tentative schedule of topic coverage

Session	Material
1	Prologue
2	Programming
3	:
4	:
5	:
6	:
7	:
8	Recursion
9	Playing Games
10	Internet
11	:
12	Cryptography
13	Computer Hardware
14	Models of Computation
15	Comparative Programming Languages
16	Human Language Translation

This is very tentative!

Questions

If you ever feel yourself getting lost:

Don't make me do all the work!

What is your problem?

If computer science is about solving problems...
What is a problem?

Problems

A problem is a set of questions with well-defined answers.

Examples

Examples of problems:

• What is 6x9?
• Who killed JFK?
• Which is the way to Emerald City?

Non-examples:

• What is the meaning of life, the universe, and everything?
• What is truth?
• Who is John Galt?

Input and output

For a set of questions, a problem's input identifies the question to be answered.

The output is the answer to the question.

Example:

Problem ADDITION:
Input: two numbers x and y.
Output: the sum x+y.

A problem instance is a particular question from the set: ``What is 6 + 9?''

More example problems

You mean we're going to study arithmetic?

Our problems won't always be obviously mathematical.

Example:

Problem CHESS:
Input: a configuration of the board.
Output: a move for white guaranteeing a win.

Example input:

     Q - - - - - - -
     - - - - K - - R  boldface = black
     - - - P - - - -  roman = white
     - - - P - - - -  P = pawn
     - - - - P - P -  R = rook
     - - - - - P - -  K = king
     - - - - - - - -  Q = queen
     - R - K - - - -

Algorithms

An algorithm is a method for solving a problem that

• always finds an answer (termination)
• is always correct (correctness)

Example: an algorithm for ADDITION

    111
    1999
  +  125
  ------
    2124

Another example

Think of algorithms for PRIMALITY.

Problem PRIMALITY:
Input: a number n that is greater than 1.
Output: true if n is prime, false if not.

Here are two algorithms. Each is written in pseudocode, a recipe-like mixture of blank space, English, and math.

First algorithm:

Algorithm Prime-Test-Exhaustive(n):
for each i from 2 to n-1, do:
  if i divides n, then:
    output false.
    stop.
  end of if
end of loop
output true.
stop.

Second algorithm:

Algorithm Prime-Test-All(n):
for each integer i from 2 to sqrt(n), do:
  if i divides n, then:
    output false.
    stop.
  end of if
end of loop
output true.
stop.

Problem MISSING-CARD:
Input: 51 cards, each labeled uniquely between 1 and 52.
Output: the number missing from the deck.

One algorithm:

Algorithm Find-Missing-Card(deck):
while deck is not in increasing order, do:
    Shuffle deck.
end of loop
Find skipped number in deck.
Say this number.

Conclusion