MIT python course all class slide

WELCOME!
(download slides and .py files from
the class site to follow along)
6.100L Lecture 1
Ana Bell
1

TODAY
 Course info
 What is computation
 Python basics
 Mathematical operations
 Python variables and types
 NOTE: slides and code files up before each lecture
 Highly encourage you to download them before class
 Take notes and run code files when I do
 Do the in-class “You try it” breaks
 Class will not be recorded
 Class will be live-Zoomed for those sick/quarantine
6.100L Lecture 1
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WHY COME TO CLASS?
 You get out of this course what you put into it
 Lectures
 Intuition for concept
 Teach you the concept
 Ask me questions!
 Examples of concept
 Opportunity to
practice practice practice
 Repeat
6.100L Lecture 1
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PRACTICE
February 3, 2016 6.0001 LECTURE 1
PROBLEM
SOLVING
PROGRAMMING
SKILL
PSETS
EXAMS
MANDATORY
FINGER
EXERCISES
OPTIONAL
(practice)
OFFICE
HOURS
PIAZZA
LECTURES
RECITATION
KNOWLEDGE
OF CONCEPTS
6.100L Lecture 1
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TOPICS
 Solving problems using computation
 Python programming language
 Organizing modular programs
 Some simple but important algorithms
 Algorithmic complexity
6.100L Lecture 1
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LET’S GOOOOO!
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TYPES of KNOWLEDGE
 Declarative knowledge is statements of fact
 Imperative knowledge is a recipe or “how-to”
 Programming is about writing recipes to generate facts
6.100L Lecture 1
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NUMERICAL EXAMPLE
 Square root of a number x is y such that y*y = x
 Start with a guess, g
1) If g*g is close enough to x, stop and say g is the answer
2) Otherwise make a new guess by averaging g and x/g
3) Using the new guess, repeat process until close enough
 Let’s try it for x = 16 and an initial guess of 3
g g*g x/g (g+x/g)/2
3 9 16/3 4.17
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WE HAVE an ALGORITHM
1) Sequence of simple steps
2) Flow of control process that specifies when each step is
executed
3) A means of determining when to stop
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ALGORITHMS are RECIPES /
RECIPES are ALGORITHMS
 Bake cake from a box
 1) Mix dry ingredients
 2) Add eggs and milk
 3) Pour mixture in a pan
 4) Bake at 350F for 5 minutes
 5) Stick a toothpick in the cake
 6a) If toothpick does not come out clean, repeat step 4 and 5
 6b) Otherwise, take pan out of the oven
 7) Eat
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COMPUTERS are MACHINES that
EXECUTE ALGORITHMS
 Two things computers do:
 Performs simple operations
100s of billions per second!
 Remembers results
100s of gigabytes of storage!
 What kinds of calculations?
 Built-in to the machine, e.g., +
 Ones that you define as the programmer
 The BIG IDEA here?
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A COMPUTER WILL ONLY DO
WHAT YOU TELL IT TO DO
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COMPUTERS are MACHINES that
EXECUTE ALGORITHMS
 Fixed program computer
 Fixed set of algorithms
 What we had until 1940’s
 Stored program computer
 Machine stores and executes instructions
 Key insight: Programs are no different from other kinds of data
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STORED PROGRAM COMPUTER
 Sequence of instructions stored inside computer
 Built from predefined set of primitive instructions
1) Arithmetic and logical
2) Simple tests
3) Moving data
 Special program (interpreter) executes each instruction in
order
 Use tests to change flow of control through sequence
 Stops when it runs out of instructions or executes a halt instruction
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6.100L Lecture 1
MEMORY
CONTROL
UNIT
ARITHMETIC
LOGIC UNIT
INPUT OUTPUT
program counter do primitive ops
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6.100L Lecture 1
MEMORY
CONTROL
UNIT
ARITHMETIC
LOGIC UNIT
INPUT OUTPUT
3456 3
3457 4
3458
3459 True
3460
3461 False
7889 5
7890 2
7891
7892
7893
7894
Add 3456 3457
Add 7889 7890
Store 3458
Store 7891
Compare 3458 7891
Print
18

6.100L Lecture 1
MEMORY
CONTROL
UNIT
ARITHMETIC
LOGIC UNIT
INPUT OUTPUT
3456 3
3457 4
3458 7
3459 True
3460
3461 False
7889 5
7890 2
7891
7892
7893
7894
Add 3456 3457
Add 7889 7890
Store 3458
Store 7891
Compare 3458 7891
Print
20

6.100L Lecture 1
MEMORY
CONTROL
UNIT
ARITHMETIC
LOGIC UNIT
INPUT OUTPUT
3456 3
3457 4
3458 7
3459 True
3460
3461 False
7889 5
7890 2
7891 7
7892
7893
7894
Add 3456 3457
Add 7889 7890
Store 3458
Store 7891
Compare 3458 7891
Print
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BASIC PRIMITIVES
 Turing showed that you can compute anything with a very
simple machine with only 6 primitives: left, right, print, scan,
erase, no op
 Real programming languages have
 More convenient set of primitives
 Ways to combine primitives to create new primitives
 Anything computable in one language is computable in any
other programming language
6.100L Lecture 1
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25

ASPECTS of LANGUAGES
 Primitive constructs
 English: words
 Programming language: numbers, strings, simple operators
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ASPECTS of LANGUAGES
 Syntax
 English: "cat dog boy"  not syntactically valid
"cat hugs boy"  syntactically valid
 Programming language: "hi"5  not syntactically valid
"hi"*5  syntactically valid
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ASPECTS of LANGUAGES
 Static semantics: which syntactically valid strings have meaning
 English: "I are hungry"  syntactically valid
but static semantic error
 PL: "hi"+5  syntactically valid
but static semantic error
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ASPECTS of LANGUAGES
 Semantics: the meaning associated with a syntactically correct
string of symbols with no static semantic errors
 English: can have many meanings "The chicken is
ready to eat."
 Programs have only one meaning
 But the meaning may not be what programmer intended
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WHERE THINGS GO WRONG
 Syntactic errors
 Common and easily caught
 Static semantic errors
 Some languages check for these before running
program
 Can cause unpredictable behavior
 No linguistic errors, but different meaning
than what programmer intended
 Program crashes, stops running
 Program runs forever
 Program gives an answer, but it’s wrong!
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