61A Lecture 1

Friday, August 24, 2012

Welcome to Berkeley Computer Science!

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The Course Staff

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The Course Staff

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The Course Staff

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The Course Staff

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0 1 2 3 4 5 years

The Course Staff

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0 1 2 3 4 5 years

TAs run sections, labs, and also everything else

The Course Staff

3

0 1 2 3 4 5 years

TAs run sections, labs, and also everything else

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

The Course Staff

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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

What is Computer Science?

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What is Computer Science?

Systems

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What is Computer Science?

Systems

Artificial Intelligence

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What is Computer Science?

Systems

Artificial Intelligence

Graphics

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What is Computer Science?

Systems

Artificial Intelligence

Graphics

Security

4

What is Computer Science?

Systems

Artificial Intelligence

Graphics

Security

Networking

Programming Languages

Theory

Scientific Computing

...

4

What is Computer Science?

Systems

Artificial Intelligence

Graphics

Security

Networking

Programming Languages

Theory

Scientific Computing

...

4

What is Computer Science?

Systems

Artificial Intelligence

Graphics

Security

Networking

Programming Languages

Theory

Scientific Computing

...

4

Computer Vision

What is Computer Science?

Systems

Artificial Intelligence

Graphics

Security

Networking

Programming Languages

Theory

Scientific Computing

...

4

Computer Vision

Planning

What is Computer Science?

Systems

Artificial Intelligence

Graphics

Security

Networking

Programming Languages

Theory

Scientific Computing

...

4

Computer Vision

Planning

Robotics

What is Computer Science?

Systems

Artificial Intelligence

Graphics

Security

Networking

Programming Languages

Theory

Scientific Computing

...

4

Computer Vision

Planning

Robotics

Natural Language Processing

What is Computer Science?

Systems

Artificial Intelligence

Graphics

Security

Networking

Programming Languages

Theory

Scientific Computing

...

4

Computer Vision

Planning

Robotics

Natural Language Processing

...

What is Computer Science?

Systems

Artificial Intelligence

Graphics

Security

Networking

Programming Languages

Theory

Scientific Computing

...

4

Computer Vision

Planning

Robotics

Natural Language Processing

...

What is 61A?

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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

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What is 61A?

• A course about managing complexity

Mastering abstraction

Not about 1’s and 0’s

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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

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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

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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

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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

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

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What is 61A?

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Plone Conference. Photo courtesy of Kriszta Szita

Alternatives to 61A

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Alternatives to 61A

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CS 61AS: Self-paced 61A

Alternatives to 61A

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CS 10: The Beauty and Joy of Computing

CS 61AS: Self-paced 61A

Course Policies

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Course Policies

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The purpose of this course is to help you learn

Course Policies

The staff is here to make you successful

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The purpose of this course is to help you learn

Course Policies

The staff is here to make you successful

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The purpose of this course is to help you learn

All the details are online:

http://inst.eecs.berkeley.edu/~cs61A/fa12/about.html

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

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Collaboration

• Discuss everything with each other

• EPA: Effort, participation, and altruism• Homework can be completed with a partner

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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

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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!

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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!

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The limits of collaboration

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!

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• One simple rule: don’t share code

The limits of collaboration

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!

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• One simple rule: don’t share code

• Copying project solutions is a serious offense!

The limits of collaboration

Announcements

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Announcements

• Next week, both section and lab will meet in the lab rooms.

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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.

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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!

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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.

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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!

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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!

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Types of expressions

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An expression

describes a computation

and evaluates to a value

18 + 69

Types of expressions

11

An expression

describes a computation

and evaluates to a value

18 + 696

23

Types of expressions

11

An expression

describes a computation

and evaluates to a value

18 + 696

23 p3493161

Types of expressions

11

An expression

describes a computation

and evaluates to a value

18 + 696

23 p3493161

sin⇡

Types of expressions

11

An expression

describes a computation

and evaluates to a value

18 + 696

23 p3493161

sin⇡

|� 1869|

Types of expressions

11

An expression

describes a computation

and evaluates to a value

18 + 696

23 p3493161

sin⇡

100X

i=1

i

|� 1869|

Types of expressions

11

An expression

describes a computation

and evaluates to a value

18 + 696

23 p3493161

sin⇡

100X

i=1

i

|� 1869|

✓69

18

◆

Types of expressions

11

An expression

describes a computation

and evaluates to a value

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

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

Call Expressions in Python

All expressions can use function call notation

(Demo)

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Anatomy of a Call Expression

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Anatomy of a Call Expression

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add ( 2 , 3 )

Anatomy of a Call Expression

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add ( 2 , 3 )

Operator

Anatomy of a Call Expression

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add ( 2 , 3 )

Operator Operand 0 Operand 1

Anatomy of a Call Expression

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add ( 2 , 3 )

Operator Operand 0 Operand 1

Operators and operands are expressions

Anatomy of a Call Expression

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add ( 2 , 3 )

Operator Operand 0 Operand 1

Operators and operands are expressions

So they evaluate to values

Anatomy of a Call Expression

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Evaluation procedure for call expressions:

add ( 2 , 3 )

Operator Operand 0 Operand 1

Operators and operands are expressions

So they evaluate to values

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

Anatomy of a Call Expression

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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

mul(add(2, mul(4, 6)), add(3, 5))

Evaluating Nested Expressions

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mul(add(2, mul(4, 6)), add(3, 5))

Evaluating Nested Expressions

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mul(add(2, mul(4, 6)), add(3, 5))

Evaluating Nested Expressions

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mul

mul(add(2, mul(4, 6)), add(3, 5))

add(2, mul(4, 6))

Evaluating Nested Expressions

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mul

mul(add(2, mul(4, 6)), add(3, 5))

add(2, mul(4, 6))

Evaluating Nested Expressions

14

mul

add

mul(add(2, mul(4, 6)), add(3, 5))

add(2, mul(4, 6))

Evaluating Nested Expressions

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mul

add 2

mul(add(2, mul(4, 6)), add(3, 5))

add(2, mul(4, 6))

Evaluating Nested Expressions

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mul

add 2mul(4, 6)

mul(add(2, mul(4, 6)), add(3, 5))

add(2, mul(4, 6))

Evaluating Nested Expressions

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mul

add 2mul(4, 6)

mul 4 6

mul(add(2, mul(4, 6)), add(3, 5))

add(2, mul(4, 6))

Evaluating Nested Expressions

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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

26mul

add 2mul(4, 6)

mul 4 6

24

mul(add(2, mul(4, 6)), add(3, 5))

add(2, mul(4, 6))

Evaluating Nested Expressions

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26mul

add 2mul(4, 6)

mul 4 6

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add(3, 5)

mul(add(2, mul(4, 6)), add(3, 5))

add(2, mul(4, 6))

Evaluating Nested Expressions

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26mul

add 2mul(4, 6)

mul 4 6

24

add(3, 5)

add 3 5

mul(add(2, mul(4, 6)), add(3, 5))

add(2, mul(4, 6))

Evaluating Nested Expressions

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26mul

add 2mul(4, 6)

mul 4 6

24

add(3, 5)

add 3 5

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208mul(add(2, mul(4, 6)), add(3, 5))

add(2, mul(4, 6))

Evaluating Nested Expressions

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26mul

add 2mul(4, 6)

mul 4 6

24

add(3, 5)

add 3 5

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Data, Functions, and Interpreters

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Data, Functions, and Interpreters

Data: The things that programs fiddle with

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Data, Functions, and Interpreters

Data: The things that programs fiddle with

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2

Data, Functions, and Interpreters

Data: The things that programs fiddle with

15

2“The Art of Computer Programming”

Data, Functions, and Interpreters

Data: The things that programs fiddle with

15

2“The Art of Computer Programming”

Donald Knuth

Data, Functions, and Interpreters

Data: The things that programs fiddle with

15

2“The Art of Computer Programming”

Donald KnuthShakespeare’s 37 plays

Data, Functions, and Interpreters

Data: The things that programs fiddle with

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2“The Art of Computer Programming”

Donald KnuthShakespeare’s 37 plays

Functions: Rules for manipulating data

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

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

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

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)

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