# Lecture 1 - What is computation ?

Computers does 2 things only:
- Calculate
- Remember Calculations

Computational problem solving & Computational complexities

From Ambiguous problem statement to computational formulation of a method

Declarative vs Imperative Knowledge
Declarative: Like an axiom - Statement of Facts
Imperative: Like an algorithm - Sequence & Flow control - Computations

John Von Newmann & Alan Turing
Fixed program computer(Arithmetic logic Unit) - 1941 - Atanasoff & Berry
Manchester Mark 1 - University of Manchester
Stored program computer
Interpreter for instrutions - Now computers can program themselves

Flow control, Flowcharts
Programming Langs

Church-Turing Thesis - Defines the concept of Computability - The Nature of Computation
Lambda calculus - Computable and effectivelly calculable
if solvable by algo solvable by turing machine and vice versa
Universality of Computation

Halting problem - if you can prove a program will run forever or not ? no said Turing

Low lvl vs High lvl
Interpreted vs Compiled

Syntax, Static sementics (syntatical validity), Sementics(meaning)

Turing: We can compute anything using 6 primitives
Read, Write, Move Left, Move Right, Scan, Do nothing

Using Tape he showed can compute anything
modern langs evolved from primitives

primitives -> expressions -> values ->  meaning

Primitve constructs(Syntax):
- English: Words
- Prog langs: Numbers, Strings, Simple operators

Static Sementics (Invalidity):
- English: I are hungry
- Prog langs: 3+"hi"

Sementics
- English: more than one meaning
- Prog langs: only one meaning

Everything in Python is an object:
- Scalar(int,float,bool(true/false),None,str)
- non-scalar(type())

<expr> = <var>

x % y = Remainder when x/y
x ** y = x to power of y

# Lecture 2 - Branching and Iteration

== vs = comparaison vs assignement

a variable is just a name, object can be assigned to more than 1 var
can assign multiples var in one line <><>=<><>

Operators can be combined to form expressions, the value of the expression is an object of some type()

## Arithmetic Operators
- +  : Addition
- -  : Subtraction
- *  : Multiplication
- /  : Division
- %  : Modulo (remainder)
- ** : Exponent
- // : Floor division

## Comparaison Operators - Compare 2 value and return true or false
- == : Equal to
- != : Nor equal to
- >  : Greater than
- <  : Less than
- >= : Greater than or equal to
- <= : Lesser than or equal to

## Assignement Operators - Assign value to var
- =  : Assignement
- += : Addition assignement
- -= : Substraction assignement
- *= : Multiplication assignement*
- /= : Division assignement
- %= : Modulo assignement
- //= : Floor division assignement
- **= : Exponentiation assignement**

## Logical Operators - To combine conditional statements
- AND
- OR
- NOT

## Bitwise Operator - Operate on Binary numbers
## Membership Operator - Check if value is part of sequence
- in
- not in
## Identity Operator - Check if 2 object refer to the same memory location
- is
- is not

Variables can't start with a number
Variables are case sensitive
Keywords(reserved words) can't be used
Identatation sementically meaningfull

Strait Line Programs VS Branching Programs (ex: with conditional expresion)


slicing = sequence[start:stop:step]
'abc'[:] = 'abc'[0:len('abc')]

when convert float to int : truncated not rounded

fstring - to format strings

f"Hello {name}, you are {age} yrs old"
can include any expression in bracket
f"The area of a circle with radius {sum(radius)} is {math.pi * radius}"

## Conditional Statement
- if : execute if true
- if, elif, else

## Looping Statement
- for - Iterate over a sequence
- while - Repeat a block of code as long as condition is true

## Control statement
- break - exit current loop
- continue - skip rest of current iteration and follow to the next iteration in loop
- pass - null does nothing

range(start,stop,increment)

PEP8 - https://peps.python.org/pep-0008/

# Lecture 3 - String Manipulation, Guess and Check, Approximations, Bisection

Algo:
- Guess and check - exhaustive enumeration
- Bisection Search - a example of succesive approximation method
- Approximation - most common: Newton-Raphson Method

# Lecture 4 - Decomposition, Abstraction, and Functions