1 Survey of Computer Science Kevin Walsh kwalsh@holycross.edu - - PowerPoint PPT Presentation

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Kevin Walsh kwalsh@holycross.edu

Survey of Computer Science

http://cs.holycross.edu/~csci110/ Readings: CSI 1, 9.2

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Motivation

Why are we here? Mandatory – required to graduate. Education – CS is neat! Profit – you will get a better job & higher pay. Fun – Really!

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What is Computer Science?

• Computer Graphics
• Numerical and Symbolic Computation
• Artificial Intelligence and Robotics
• Algorithms and Data Structures
• Computer Architecture
• Database and Information Retrieval
• Human-Computer Communication
• Software Engineering
• Operating Systems
• Programming Languages (& Compiler Construction)
• Social, Ethical and Professional Context

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What is Computer Science?

The study of imperative knowledge Imperative Knowledge— How to do something Declarative Knowledge— What something is (by definition)

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What is Computer Science?

What can be (efficiently) computed or automated? + How can we do it in practice? + Let's build things! + What did we build?

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What is Computer Science?

The only science that builds what we measure. – John Day

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Problem Solving

Big Idea #1 Recursion: You can define something in terms of itself. Example: Divide & Conquer

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Problem Solving

Big Idea #1 Recursion: You can define something in terms of itself. Example: Divide & Conquer

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Abstraction

Big Idea #2 Abstraction: Giving something a name makes it more useful. Example:

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Computing

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Computing

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Programs, Inputs, & Outputs

Abstractions:

• Pixel: a dot; has a color and a location
• Image file: a set of pixels; can be read, saved, etc.
• Program: a recipe for computing; takes an input and an
• utput; can be executed, etc.

input file: image1 Program:

• 1. read from input file
• 2. for each pixel x in the file:

compute 7 – x

• 3. save to output file
• utput

file: image2

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Encoding

Big Idea #3: Encoding: Most everything can be encoded as data. (And you can compute with data.) Examples:

• Text files (doc, txt, …)
• Image files (jpg, gif, …)
• Audio files (mp3, wma, …)
• Video files (DVD, youtube, …)
• Mixed media (web pages, powerpoint, …)
• Programs (exe, dll, …)

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Black Box Abstraction

Big Idea #4 Black Box Abstraction: We can use something without knowing (precisely) how it works.

Applications Program Modules High Level Programming Language Assembly Language Machine Code / Digital Circuits

We can start in the middle!

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Complexity

Abstraction lets us manage complexity. +

~ 2 million lines of code ~ 50 million lines of code ~ 2 billion transistors

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10001100101010010000000000000000 10001100101010100000000000000100 10001100101010110000000000001000 00000001001010100001100000100000 00000000011010110001100000100000 00000011111000000000000000001000 00100000000001010000001111101000 00001100000100000000000000000000 10101100101000110000000000001100

Machine Code

Machine code is binary coded instructions (sequences of 0’s and 1’s).

• Used directly by computer hardware
• Not fit for human consumption
• Not portable: e.g. MIPS, ARM, x86, …
• Runs only on specific type of computer

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High Level Languages

High-level languages are closer to natural languages.

• A compiler translates a high-level language

program into a machine language program

input = read_image_file(‘mario.png’) for each x in input.pixels(): y = 7 – x

• utput.add_pixel(y)

write_image_file(output, ‘scary.png’)

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High Level Languages

High-level languages are closer to natural languages.

• A compiler translates a high-level language

program into a machine language program

• Portable (in principle)
• Examples: C, C++, C#, Javascript, Perl, Ruby, Java,

Python, FORTRAN, ALGOL, COBOL, Visual Basic, Ada, Lisp, Scheme

– Each language has a separate set of compilers (as many as one for each machine language!)

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Early and Late Compilation

• 1. Programmer writes program in a high-level

language.

• 2. A compiler translates it into a machine language

program.

• 3. The computer executes the result.

(which the computer later executes).

x86 machine code

C++ to x86 Compiler

C++ source code

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Early and Late Compilation

An interpreter translates a high-level language program into a machine language program and immediately executes it.

• Can even be interactive!

Python Interpreter

Python source code

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Which Language to use?

Which high-level language to use? C, C++, C#, Javascript, perl, php, ruby, Java, Pascal, Python, Visual Basic, smalltalk, mathmatica, maple, Lisp, Scheme, ML, assembly, Postscript, FORTRAN, ALGOL, COBOL, … Learn one, learn them all!

• Same control structures and main ideas

 The concepts transfer between languages. Python is a good teaching language

• Interactive: try out programs as you write them
• Easier to learn than many other languages
• Lots of existing code, examples, tutorials, etc.
• Actually useful in the real world

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Administration

http://cs.holycross.edu/~csci110/

• Lecture slides & schedule, assignments, etc.
• Links to Python compiler & documentation

Office Hours

– Mondays 1pm – 3pm – Thursdays 10am – noon – By appointment

Grading:

• Lab worksheets

(10%)

• Homework & projects

(30 %)

• Two midterm exams

(20 % each)

• Final exam

(20%)

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Academic Integrity

Academic Integrity:

• All submitted work must be your own (or your groups)

– OK to study together, but do not share solutions

• Cite your sources

Stressed? Tempted? Lost?

• Come see me before due date!

Plagiarism in any form will not be tolerated.

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Announcements

Announcements Questionnaire – Due Friday before class Lab 1 – Wednesday Readings for today – CSI 1, 9.2 Readings for Friday – CSI 9.4, Py 1.1-1.5 Enrollment – There is plenty of room in Section 02!