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CS Fundamentals: Algorithm, Abstraction, Automation, and More - Prof. Gary Locklair, Study notes of Computer Science

An overview of the fundamental concepts in computer science (cs), including algorithms, abstraction, automation, information, interface, intelligence, cognition, complexity, and creation. Definitions, examples, and illustrations of these concepts in the context of using various computer applications such as a word processor, web browser, and programming process.

Typology: Study notes

Pre 2010

Uploaded on 07/23/2009

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Day 27 - 5 December 2006
Exam review
9 Grand Ideas
1. Algorithm
{test} Definition: detailed and unambiguous set of step-by-step
procedures for solving a problem
Brookshear quotes
Concise definition of CS is “problem solving”
Easy to see the connection between CS and algorithms
Effective algorithm:
computable
finite
Big-O
measures the complexity of algorithms
used to compare two algorithms – time/space (chp 15)
detect …
control structures:
sequence
selection
repetition (**)
procedure call (*)
* impact on “big O”
Why? There are multiple, valid algorithms for the same problem
ex: searching, sorting
2. Abstraction
{test} Definition: simplification by agreement
note: not fair to just simplify (I made up my own language and it
answers the test questions perfectly :-) – we use standard
abbreviations to simplify; that is, there is an agreement
connotes: summary, generalization
way to deal with complexity
CSC 490 Course Notes and Outline, © Dr. Gary Locklair, Fall 2006
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Day 27 - 5 December 2006 Exam review 9 Grand Ideas

  1. Algorithm {test} Definition: detailed and unambiguous set of step-by-step procedures for solving a problem Brookshear quotes Concise definition of CS is “problem solving” Easy to see the connection between CS and algorithms Effective algorithm: computable finite Big-O measures the complexity of algorithms used to compare two algorithms – time/space (chp 15) detect … control structures: sequence selection repetition (*) procedure call () * impact on “big O” Why? There are multiple, valid algorithms for the same problem ex: searching, sorting
  2. Abstraction {test} Definition: simplification by agreement note: not fair to just simplify (I made up my own language and it answers the test questions perfectly :-) – we use standard abbreviations to simplify; that is, there is an agreement connotes: summary, generalization way to deal with complexity

ex: object-oriented “information hiding” using a built-in function, eg cout don’t have to know details, only “big picture”

  1. Automation {test} Definition: runs autonomously (“by itself”) much power, but also sometimes not noticed users may believe that they are “running” computer, especially with interactive processing, but this is not the case ex: web server – automatically services all requests for web pages concept of feedback – outside inputs regulate function ex: thermostat – heater Illustrate these three in context of using a word processor…
  2. Information {test} Definition: [students define] “Information Processing” is a slight misnomer; computers don’t process information as information is the ultimate goal goal of computer: convert input data into output information Information Theory (not Shannon theory) source of information is intelligence; information comes from intelligence; intelligence is necessary to create information ex: book – recognize intelligent author via the information presented
  3. Interface (noun) {test} Definition: [students define] Because of layers of abstractions, interfaces are built Ex: User I/F Method of communications and control between user and computer User: data and commands Computer: information and messages

Illustrate these three in the context of the programming process