61a lecture 1 - university of california, berkeleycs61a/fa12/slides/01-functions_1pp.… · the...
TRANSCRIPT
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61A Lecture 1
Friday, August 24, 2012
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Welcome to Berkeley Computer Science!
2
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The Course Staff
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The Course Staff
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The Course Staff
3
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The Course Staff
3
0 1 2 3 4 5 years
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The Course Staff
3
0 1 2 3 4 5 years
TAs run sections, labs, and also everything else
![Page 8: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/8.jpg)
The Course Staff
3
0 1 2 3 4 5 years
TAs run sections, labs, and also everything else
![Page 9: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/9.jpg)
The Course Staff
3
0 1 2 3 4 5 years
TAs run sections, labs, and also everything else
Readers are your personal programming mentors
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The Course Staff
3
0 1 2 3 4 5 years
TAs run sections, labs, and also everything else
Readers are your personal programming mentorsLab Assistants ensure that you don’t get stuck
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What is Computer Science?
4
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What is Computer Science?
Systems
4
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What is Computer Science?
Systems
Artificial Intelligence
4
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What is Computer Science?
Systems
Artificial Intelligence
Graphics
4
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What is Computer Science?
Systems
Artificial Intelligence
Graphics
Security
4
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What is Computer Science?
Systems
Artificial Intelligence
Graphics
Security
Networking
Programming Languages
Theory
Scientific Computing
...
4
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What is Computer Science?
Systems
Artificial Intelligence
Graphics
Security
Networking
Programming Languages
Theory
Scientific Computing
...
4
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What is Computer Science?
Systems
Artificial Intelligence
Graphics
Security
Networking
Programming Languages
Theory
Scientific Computing
...
4
Computer Vision
![Page 19: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/19.jpg)
What is Computer Science?
Systems
Artificial Intelligence
Graphics
Security
Networking
Programming Languages
Theory
Scientific Computing
...
4
Computer Vision
Planning
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What is Computer Science?
Systems
Artificial Intelligence
Graphics
Security
Networking
Programming Languages
Theory
Scientific Computing
...
4
Computer Vision
Planning
Robotics
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What is Computer Science?
Systems
Artificial Intelligence
Graphics
Security
Networking
Programming Languages
Theory
Scientific Computing
...
4
Computer Vision
Planning
Robotics
Natural Language Processing
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What is Computer Science?
Systems
Artificial Intelligence
Graphics
Security
Networking
Programming Languages
Theory
Scientific Computing
...
4
Computer Vision
Planning
Robotics
Natural Language Processing
...
![Page 23: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/23.jpg)
What is Computer Science?
Systems
Artificial Intelligence
Graphics
Security
Networking
Programming Languages
Theory
Scientific Computing
...
4
Computer Vision
Planning
Robotics
Natural Language Processing
...
![Page 24: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/24.jpg)
What is 61A?
5
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What is 61A?
• A course about managing complexity
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What is 61A?
• A course about managing complexity
Mastering abstraction
5
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What is 61A?
• A course about managing complexity
Mastering abstraction
Not about 1’s and 0’s
5
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What is 61A?
• A course about managing complexity
Mastering abstraction
Not about 1’s and 0’s
• An introduction to Python
5
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What is 61A?
• A course about managing complexity
Mastering abstraction
Not about 1’s and 0’s
• An introduction to Python
All the features we really need: introduced today
5
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What is 61A?
• A course about managing complexity
Mastering abstraction
Not about 1’s and 0’s
• An introduction to Python
All the features we really need: introduced today
Understanding through implementation
5
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What is 61A?
• A course about managing complexity
Mastering abstraction
Not about 1’s and 0’s
• An introduction to Python
All the features we really need: introduced today
Understanding through implementation
Programs that run other programs: meta-evaluation
5
![Page 32: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/32.jpg)
What is 61A?
• A course about managing complexity
Mastering abstraction
Not about 1’s and 0’s
• An introduction to Python
All the features we really need: introduced today
Understanding through implementation
Programs that run other programs: meta-evaluation
• A challenging course that will demand a lot of you
5
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What is 61A?
6
Plone Conference. Photo courtesy of Kriszta Szita
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Alternatives to 61A
7
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Alternatives to 61A
7
CS 61AS: Self-paced 61A
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Alternatives to 61A
7
CS 10: The Beauty and Joy of Computing
CS 61AS: Self-paced 61A
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Course Policies
8
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Course Policies
8
The purpose of this course is to help you learn
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Course Policies
The staff is here to make you successful
8
The purpose of this course is to help you learn
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Course Policies
The staff is here to make you successful
8
The purpose of this course is to help you learn
All the details are online:
http://inst.eecs.berkeley.edu/~cs61A/fa12/about.html
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Collaboration
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Collaboration
• Discuss everything with each other
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Collaboration
• Discuss everything with each other
• EPA: Effort, participation, and altruism
9
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Collaboration
• Discuss everything with each other
• EPA: Effort, participation, and altruism• Homework can be completed with a partner
9
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Collaboration
• Discuss everything with each other
• EPA: Effort, participation, and altruism• Homework can be completed with a partner
• Projects should be completed with a partner
9
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Collaboration
• Discuss everything with each other
• EPA: Effort, participation, and altruism• Homework can be completed with a partner
• Projects should be completed with a partner
• Find a project partner in your section!
9
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Collaboration
• Discuss everything with each other
• EPA: Effort, participation, and altruism• Homework can be completed with a partner
• Projects should be completed with a partner
• Find a project partner in your section!
9
The limits of collaboration
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Collaboration
• Discuss everything with each other
• EPA: Effort, participation, and altruism• Homework can be completed with a partner
• Projects should be completed with a partner
• Find a project partner in your section!
9
• One simple rule: don’t share code
The limits of collaboration
![Page 49: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/49.jpg)
Collaboration
• Discuss everything with each other
• EPA: Effort, participation, and altruism• Homework can be completed with a partner
• Projects should be completed with a partner
• Find a project partner in your section!
9
• One simple rule: don’t share code
• Copying project solutions is a serious offense!
The limits of collaboration
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Announcements
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Announcements
• Next week, both section and lab will meet in the lab rooms.
10
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Announcements
• Next week, both section and lab will meet in the lab rooms.
• Homework 1 is posted! All homework is graded on effort.
10
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Announcements
• Next week, both section and lab will meet in the lab rooms.
• Homework 1 is posted! All homework is graded on effort.
• If you are on the waitlist, still complete assignments!
10
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Announcements
• Next week, both section and lab will meet in the lab rooms.
• Homework 1 is posted! All homework is graded on effort.
• If you are on the waitlist, still complete assignments!
• Midterms are on 9/19 and 10/24. Final exam is on 12/13.
10
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Announcements
• Next week, both section and lab will meet in the lab rooms.
• Homework 1 is posted! All homework is graded on effort.
• If you are on the waitlist, still complete assignments!
• Midterms are on 9/19 and 10/24. Final exam is on 12/13.
• Read the lecture notes before you come to lecture!
10
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Announcements
• Next week, both section and lab will meet in the lab rooms.
• Homework 1 is posted! All homework is graded on effort.
• If you are on the waitlist, still complete assignments!
• Midterms are on 9/19 and 10/24. Final exam is on 12/13.
• Read the lecture notes before you come to lecture!
10
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Types of expressions
11
An expression
describes a computation
and evaluates to a value
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18 + 69
Types of expressions
11
An expression
describes a computation
and evaluates to a value
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18 + 696
23
Types of expressions
11
An expression
describes a computation
and evaluates to a value
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Types of expressions
11
An expression
describes a computation
and evaluates to a value
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23 p3493161
sin⇡
Types of expressions
11
An expression
describes a computation
and evaluates to a value
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18 + 696
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sin⇡
|� 1869|
Types of expressions
11
An expression
describes a computation
and evaluates to a value
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sin⇡
100X
i=1
i
|� 1869|
Types of expressions
11
An expression
describes a computation
and evaluates to a value
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23 p3493161
sin⇡
100X
i=1
i
|� 1869|
✓69
18
◆
Types of expressions
11
An expression
describes a computation
and evaluates to a value
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18 + 696
23 p3493161
sin⇡
f(x)100X
i=1
i
|� 1869|
✓69
18
◆
Types of expressions
11
An expression
describes a computation
and evaluates to a value
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18 + 696
23 p3493161
sin⇡
f(x)100X
i=1
i
|� 1869|
✓69
18
◆
Types of expressions
11
An expression
describes a computation
and evaluates to a value
![Page 67: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/67.jpg)
Call Expressions in Python
All expressions can use function call notation
(Demo)
12
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Anatomy of a Call Expression
13
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Anatomy of a Call Expression
13
add ( 2 , 3 )
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Anatomy of a Call Expression
13
add ( 2 , 3 )
Operator
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Anatomy of a Call Expression
13
add ( 2 , 3 )
Operator Operand 0 Operand 1
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Anatomy of a Call Expression
13
add ( 2 , 3 )
Operator Operand 0 Operand 1
Operators and operands are expressions
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Anatomy of a Call Expression
13
add ( 2 , 3 )
Operator Operand 0 Operand 1
Operators and operands are expressions
So they evaluate to values
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Anatomy of a Call Expression
13
Evaluation procedure for call expressions:
add ( 2 , 3 )
Operator Operand 0 Operand 1
Operators and operands are expressions
So they evaluate to values
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Anatomy of a Call Expression
13
Evaluation procedure for call expressions:
add ( 2 , 3 )
Operator Operand 0 Operand 1
Operators and operands are expressions
1. Evaluate the operator and operand subexpressions
So they evaluate to values
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Anatomy of a Call Expression
13
Evaluation procedure for call expressions:
add ( 2 , 3 )
Operator Operand 0 Operand 1
Operators and operands are expressions
1. Evaluate the operator and operand subexpressions
2. Apply the function that is the value of the operator subexpression to the arguments that are the values of the operand subexpression
So they evaluate to values
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mul(add(2, mul(4, 6)), add(3, 5))
Evaluating Nested Expressions
14
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mul(add(2, mul(4, 6)), add(3, 5))
Evaluating Nested Expressions
14
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mul(add(2, mul(4, 6)), add(3, 5))
Evaluating Nested Expressions
14
mul
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mul(add(2, mul(4, 6)), add(3, 5))
add(2, mul(4, 6))
Evaluating Nested Expressions
14
mul
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mul(add(2, mul(4, 6)), add(3, 5))
add(2, mul(4, 6))
Evaluating Nested Expressions
14
mul
add
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mul(add(2, mul(4, 6)), add(3, 5))
add(2, mul(4, 6))
Evaluating Nested Expressions
14
mul
add 2
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mul(add(2, mul(4, 6)), add(3, 5))
add(2, mul(4, 6))
Evaluating Nested Expressions
14
mul
add 2mul(4, 6)
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mul(add(2, mul(4, 6)), add(3, 5))
add(2, mul(4, 6))
Evaluating Nested Expressions
14
mul
add 2mul(4, 6)
mul 4 6
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mul(add(2, mul(4, 6)), add(3, 5))
add(2, mul(4, 6))
Evaluating Nested Expressions
14
mul
add 2mul(4, 6)
mul 4 6
24
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mul(add(2, mul(4, 6)), add(3, 5))
add(2, mul(4, 6))
Evaluating Nested Expressions
14
26mul
add 2mul(4, 6)
mul 4 6
24
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mul(add(2, mul(4, 6)), add(3, 5))
add(2, mul(4, 6))
Evaluating Nested Expressions
14
26mul
add 2mul(4, 6)
mul 4 6
24
add(3, 5)
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mul(add(2, mul(4, 6)), add(3, 5))
add(2, mul(4, 6))
Evaluating Nested Expressions
14
26mul
add 2mul(4, 6)
mul 4 6
24
add(3, 5)
add 3 5
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mul(add(2, mul(4, 6)), add(3, 5))
add(2, mul(4, 6))
Evaluating Nested Expressions
14
26mul
add 2mul(4, 6)
mul 4 6
24
add(3, 5)
add 3 5
8
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208mul(add(2, mul(4, 6)), add(3, 5))
add(2, mul(4, 6))
Evaluating Nested Expressions
14
26mul
add 2mul(4, 6)
mul 4 6
24
add(3, 5)
add 3 5
8
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Data, Functions, and Interpreters
15
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Data, Functions, and Interpreters
Data: The things that programs fiddle with
15
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Data, Functions, and Interpreters
Data: The things that programs fiddle with
15
2
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Data, Functions, and Interpreters
Data: The things that programs fiddle with
15
2“The Art of Computer Programming”
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Data, Functions, and Interpreters
Data: The things that programs fiddle with
15
2“The Art of Computer Programming”
Donald Knuth
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Data, Functions, and Interpreters
Data: The things that programs fiddle with
15
2“The Art of Computer Programming”
Donald KnuthShakespeare’s 37 plays
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Data, Functions, and Interpreters
Data: The things that programs fiddle with
15
2“The Art of Computer Programming”
Donald KnuthShakespeare’s 37 plays
Functions: Rules for manipulating data
![Page 98: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/98.jpg)
Data, Functions, and Interpreters
Data: The things that programs fiddle with
15
2“The Art of Computer Programming”
Donald KnuthShakespeare’s 37 plays
Functions: Rules for manipulating data
Add up numbers
![Page 99: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/99.jpg)
Data, Functions, and Interpreters
Data: The things that programs fiddle with
15
2“The Art of Computer Programming”
Donald KnuthShakespeare’s 37 plays
Functions: Rules for manipulating data
Add up numbers
Count the words in a line of text
![Page 100: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/100.jpg)
Data, Functions, and Interpreters
Data: The things that programs fiddle with
15
2“The Art of Computer Programming”
Donald KnuthShakespeare’s 37 plays
Functions: Rules for manipulating data
Add up numbersPronounce someone’s name
Count the words in a line of text
![Page 101: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/101.jpg)
Data, Functions, and Interpreters
Data: The things that programs fiddle with
15
2“The Art of Computer Programming”
Donald KnuthShakespeare’s 37 plays
Functions: Rules for manipulating data
Add up numbersPronounce someone’s name
Count the words in a line of text
(Ka-NOOTH)
![Page 102: 61A Lecture 1 - University of California, Berkeleycs61a/fa12/slides/01-Functions_1pp.… · The Course Staff 3 0 1 2 3 4 5 years TAs run sections, labs, and also everything else Readers](https://reader034.vdocuments.us/reader034/viewer/2022042322/5f0cd89b7e708231d4376b8d/html5/thumbnails/102.jpg)
Data, Functions, and Interpreters
Data: The things that programs fiddle with
15
2“The Art of Computer Programming”
Donald KnuthShakespeare’s 37 plays
Functions: Rules for manipulating data
Add up numbersPronounce someone’s name
Count the words in a line of text
(Ka-NOOTH)
Interpreter: An implementation of the procedure for evaluation