Ab Abstra raction Lecturer Michael Ball UC Berkeley | Computer - - PowerPoint PPT Presentation

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Ab Abstra raction Lecturer Michael Ball UC Berkeley | Computer - - PowerPoint PPT Presentation

Mar 16, 2023 280 likes •648 views

Co Computational Structures in Data Science UC Berkeley EECS Ab Abstra raction Lecturer Michael Ball UC Berkeley | Computer Science 88 | Michael Ball Ab Abstra raction Detail removal The act of leaving out of consideration one or

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Co Computational Structures in Data Science

UC Berkeley EECS Lecturer Michael Ball

UC Berkeley | Computer Science 88 | Michael Ball

Ab Abstra raction

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UC Berkeley | Computer Science 88 | Michael Ball

  • Detail removal

“The act of leaving out of consideration

  • ne or more properties of a complex
  • bject so as to attend to others.”
  • Generalization

“The process of formulating general concepts by abstracting common properties of instances”

  • Technical terms: Compression,

Quantization, Clustering, Unsupervized Learning

Ab Abstra raction

Henri Matisse “Naked Blue IV”

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UC Berkeley | Computer Science 88 | Michael Ball

Ex Experime ment

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UC Berkeley | Computer Science 88 | Michael Ball

Wh Where re a are re yo you f fro rom?

Possible Answers:

  • Planet Earth
  • Europe
  • California
  • The Bay Area
  • San Mateo
  • 1947 Center Street, Berkeley,

CA

  • 37.8693° N, 122.2696° W

All correct but different levels of abstraction!

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UC Berkeley | Computer Science 88 | Michael Ball

Ab Abstra raction g gone w wro rong!

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UC Berkeley | Computer Science 88 | Michael Ball

  • You’ll want to look at only the

interesting data, leave out the details, zoom in/out…

  • Abstraction is the idea that

you focus on the essence, the cleanest way to map the messy real world to one you can build

  • Experts are often brought in to

know what to remove and what to keep!

De Detail Re Remo moval (in Da Data Science)

The London Underground 1928 Map & the 1933 map by Harry Beck.

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01/28/19 UCB CS88 Sp19 L1

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UC Berkeley | Computer Science 88 | Michael Ball

  • Examples:

– Functions (e.g., sin x) – Hiring contractors – Application Programming Interfaces

(APIs)

– Technology (e.g., cars)

  • Amazing things are built when

these layer

– And the abstraction layers are

getting deeper by the day!

Th The Powe wer of Abstraction, Ev Everywh where!

Abstraction Barrier (Interface)

(the interface, or specification, or contract)

Below the abstraction line

This is where / how / when / by whom it is actually built, which is done according to the interface, specification, or contract. We only need to worry about the interface, or specification, or contract NOT how (or by whom) it’s built

Above the abstraction line

01/19/18 UCB CS88 Sp18 L1

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UC Berkeley | Computer Science 88 | Michael Ball

  • Abstraction is not universal without loss of information

(mathematically provable). This means, in the end, the complexity can only be “moved around”

  • Abstraction makes us

forget how things actually work and can therefore hide bias. Example: AI and hiring decisions.

  • Abstraction makes things special and that creates dependencies.

Dependencies grow longer and longer over time and can become unmanageable.

Ab Abstra raction: P Pitfalls

01/19/18 UCB CS88 Sp18 L1

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UC Berkeley | Computer Science 88 | Michael Ball

Al Algori rithm

  • An algorithm (pronounced AL-go-rith-um) is a procedure or formula to

solve a problem.

  • An algorithm is a sequence of instructions to change the state of a
  • system. For example: A computer’s memory, your brain (math), or the

ingredients to prepare food (cooking recipe). Think Data 8: Change or retrieve the content of a table.

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UC Berkeley | Computer Science 88 | Michael Ball

Al Algori rithm: P Pro ropert rties

  • An algorithm is a description that can be expressed within a finite

amount of space and time.

  • Executing the algorithm may take infinite space and/or time, e.g.

``calculate all prime numbers”.

  • In CS and math, we prefer to use well-defined formal languages for

defining an algorithm.

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UC Berkeley | Computer Science 88 | Michael Ball

Al Algori rithm: We Well-De Definition

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UC Berkeley | Computer Science 88 | Michael Ball

Al Algori rithms E Earl rly I y In L Life ( (1st

st Gr

Grade)

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7 8 + 5 1

  • perands
  • perator

least significant digit of result carry (MSD)

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UC Berkeley | Computer Science 88 | Michael Ball

Al Algori rithms E Earl rly I y In L Life ( (In B Binary) ry)

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+

  • perands
  • perator

LSB result carry (MSD) 1 1 1 1 1 1 1 1 1 1 12 14 26 +

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UC Berkeley | Computer Science 88 | Michael Ball

Mo More re Term rminology gy (Intuitive ve)

Code A sequence of symbols used for communication between systems (brains, computers, brain-to-computer) Data Observations Information Reduction of uncertainty in a model (measured in bits)

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UC Berkeley | Computer Science 88 | Michael Ball

Da Data or Co Code?

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UC Berkeley | Computer Science 88 | Michael Ball

Da Data or Co Code?

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UC Berkeley | Computer Science 88 | Michael Ball

Da Data or Co Code?

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Here is some information!

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UC Berkeley | Computer Science 88 | Michael Ball

Da Data or Co Code? Abs bstraction!

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Compiler or Interpreter Here: Python Human-readable code (programming language) Machine-executable instructions (byte code)

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UC Berkeley | Computer Science 88 | Michael Ball

Co Code or GU GUI: More Abs bstraction!

  • Big Idea: Layers of Abstraction

– The GUI look and feel is built out of files, directories, system code, etc.

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UC Berkeley | Computer Science 88 | Michael Ball

Re Review: w:

  • Abstraction:

– Detail Removal or Generalizations

  • Code:

– Is an abstraction! – Can be instructions or information

Computer Science is the study of abstraction

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UC Berkeley | Computer Science 88 | Michael Ball

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Co Computational Structures in Data Science

UC Berkeley EECS Lecturer Michael Ball

UC Berkeley | Computer Science 88 | Michael Ball

Py Python: Statements and Functions

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UC Berkeley | Computer Science 88 | Michael Ball

Le Learning g Obj bjectives

  • Evaluate Python Expressions
  • Call Functions in Python
  • Assign data to Variables

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UC Berkeley | Computer Science 88 | Michael Ball

Le Let’s talk Py Python

  • Expression

3.1 * 2.6

  • Call expression

max(0, x)

  • Variables

my_name

  • Assignment Statement

x = <expression>

  • Define Statement:
  • Control Statements: if …

for … while … list comprehension

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def <function name> (<argument list>) :

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Co Computational Structures in Data Science

UC Berkeley EECS Lecturer Michael Ball

UC Berkeley | Computer Science 88 | Michael Ball

Py Python: De Definitions and Co Control

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UC Berkeley | Computer Science 88 | Michael Ball

Le Learning g Obj bjectives

  • Create your own functions.
  • Use if and else to control the flow of code.

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UC Berkeley | Computer Science 88 | Michael Ball

Co Conditional Stateme ment

  • Do some statements, conditional on a predicate expression
  • Example:

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if <predicate>: <true statements> else: <false statements> if (temperature>37.2): print(“fever!”) else: print(“no fever”)

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UC Berkeley | Computer Science 88 | Michael Ball

De Defining Functions

  • Abstracts an expression or set of statements to apply to lots of instances
  • f the problem
  • A function should do one thing well

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expression

def <function name> (<argument list>) : return

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UC Berkeley | Computer Science 88 | Michael Ball

Func Functi tions: ns: Exam ampl ple

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UC Berkeley | Computer Science 88 | Michael Ball

Ho How w to Write a Good Function

  • Give a descriptive name

– Function names should be lowercase. If necessary, separate words by underscores

to improve readability. Names are extremely suggestive!

  • Chose meaningful parameter names

– Again, names are extremely suggestive.

  • Write the docstring to explain what it does

– What does the function return? What are corner cases for parameters?

  • Write doctest to show what it should do

– Before you write the implementation.

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Python Style Guide: https://www.python.org/dev/peps/pep-0008

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Co Computational Structures in Data Science

UC Berkeley EECS Lecturer Michael Ball

UC Berkeley | Computer Science 88 | Michael Ball

Func Functi tions ns and and Env nvironm nments ents

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UC Berkeley | Computer Science 88 | Michael Ball

Func Functi tions: ns: Cal alling ng and and Retur eturni ning ng Resul esults ts

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Python Tutor def max(x, y): return x if x > y else y x = 3 y = 4 + max(17, x + 6) * 0.1 z = x / y

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Co Computational Structures in Data Science

UC Berkeley EECS Lecturer Michael Ball

UC Berkeley | Computer Science 88 | Michael Ball

It Iteration W n With W While le L Loops

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UC Berkeley | Computer Science 88 | Michael Ball

Le Learning g Obj bjectives

  • Write functions that call functions
  • Learn How to use while loops.

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UC Berkeley | Computer Science 88 | Michael Ball

while St Statem ement ent – It Iteration C n Cont ntrol

  • Repeat a block of statements until a predicate expression is satisfied

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<initialization statements>

while <predicate expression>:

<body statements> <rest of the program>