Bihar STET Computer Science – Study Notes

1. Basic Web Development

1.1 Internet and WWW

Internet is a global network of interconnected computer networks.

WWW (World Wide Web) is a service running over the Internet that provides interconnected web pages/resources.

Important terms

• Web Browser – Software used to access web pages.
  • Chrome
  • Firefox
  • Edge
• Web Server – Computer/software that serves web resources.
• Website – Collection of related web pages.
• Web Page – Individual document displayed in a browser.
• URL – Uniform Resource Locator; identifies the location of a resource.
• HTTP – HyperText Transfer Protocol.
• HTTPS – Secure version of HTTP using TLS.

1.2 HTML

HTML = HyperText Markup Language

HTML is used to structure web pages.

Example:

Welcome to Quizer
Learn. Practice. Compete. Succeed.

1.3 css

CSS = Cascading Style Sheets

CSS controls the presentation/style of web pages.

It can control:

• Colors
• Fonts
• Spacing
• Borders
• Layout
• Responsive design

Example:

h1 {
    font-size: 30px;
}

Three ways to use CSS

1. Inline CSS
2. Internal CSS
3. External CSS

1.4 JavaScript

JavaScript is primarily used to add behavior and interactivity to web pages.

Examples:

• Form validation
• Dynamic content
• Events
• Calculations
• DOM manipulation

Basic example

function welcome() {
    alert("Welcome!");
}

Easy way to remember

HTML → Structure

CSS → Presentation

JavaScript → Behavior

1.5 Client-Side and Server-Side

Client-side

Code runs mainly in the user's browser.

Example:

JavaScript

Server-side

Code runs on the server.

Examples:

• PHP
• Python
• Java
• Node.js

2. Theory of Computation

Theory of Computation studies mathematical models of computation and the problems that machines can solve.

For Bihar STET, focus particularly on:

• Alphabet
• Strings
• Languages
• Finite Automata
• Regular Expressions
• Regular Languages
• Grammar
• CFG
• PDA
• Turing Machine

2.1 Alphabet

An alphabet (Σ) is a finite, non-empty set of symbols.

Example:

Σ = {0,1}

2.2 String

A string is a finite sequence of symbols from an alphabet.

Example:

If:

Σ = {a,b}

then:

abba

is a string.

Length of string

For:

w = abba

|w| = 4

Empty string

The empty string is represented by:

ε

Its length is:

|ε| = 0

2.3 Language

A language is a set of strings over an alphabet.

Example:

L = {a, ab, abb, abbb, ...}

2.4 Finite Automata

Finite Automata are mathematical models used to recognize regular languages.

Two major types:

1. DFA
2. NFA

2.5 DFA

DFA = Deterministic Finite Automaton

For each state and input symbol, there is exactly one transition.

A DFA can be represented by:

(Q, Σ, δ, q₀, F)

where:

• Q = finite set of states
• Σ = input alphabet
• δ = transition function
• q₀ = initial state
• F = set of final states

For DFA:

δ : Q × Σ → Q

2.6 NFA

NFA = Nondeterministic Finite Automaton

A state may have:

• Multiple possible transitions for the same input
• No transition for a particular input
• Depending on formalism, ε-transitions may be allowed

NFA and DFA have the same computational power for regular languages.

2.7 Regular Expression

A regular expression describes a regular language.

Important operators:

• Union: + or |
• Concatenation
• Kleene Star: *

Example:

a*

represents:

{ε, a, aa, aaa, ...}

2.8 Grammar

A grammar describes how strings in a language can be generated.

A formal grammar can be represented as:

G = (V, T, P, S)

where:

• V = Variables/non-terminals
• T = Terminals
• P = Productions
• S = Start symbol

2.9 Context-Free Grammar

CFG = Context-Free Grammar

CFGs are used to describe context-free languages.

They are particularly important in:

• Programming language syntax
• Parsing
• Compiler design

2.10 Pushdown Automata

PDA = Pushdown Automaton

A PDA is essentially a finite automaton with a stack.

It is used to recognize context-free languages.

Important relationship

Finite Automata → Regular Languages

PDA → Context-Free Languages

Turing Machine → Much more general class of computable languages/problems

2.11 Turing Machine

A Turing Machine (TM) is a mathematical model of general computation.

It contains:

• Infinite/unbounded tape in the standard idealized model
• Read/write head
• States
• Transition rules

Turing Machines are more powerful than finite automata and pushdown automata.

3. E-Commerce

E-Commerce = Electronic Commerce

It refers to buying and selling goods/services using electronic networks, particularly the Internet.

Examples:

• Online shopping
• Online ticket booking
• Online banking
• Digital payments

3.1 Types of E-Commerce

B2C

Business to Consumer

Example:

Online retailer selling directly to a customer.

B2B

Business to Business

Example:

Manufacturer selling products to another business.

C2C

Consumer to Consumer

Example:

One consumer selling an item to another consumer through an online marketplace.

C2B

Consumer to Business

Example:

An individual provides a service/product to a business.

B2G

Business to Government

Business transactions involving government organizations.

3.2 Advantages

• 24×7 availability
• Global reach
• Convenience
• Lower operating costs
• Easy comparison of products
• Faster transactions

Disadvantages

• Security risks
• Privacy concerns
• Fraud
• Dependence on Internet
• No physical inspection before purchase
• Delivery issues

3.3 E-Commerce Security

Important concepts:

• Authentication
• Authorization
• Encryption
• Digital signatures
• Secure payment
• SSL/TLS
• Firewalls

HTTPS

HTTPS protects HTTP communication using TLS.

4. Multimedia

Multimedia means integration of multiple forms of information/content.

Common components:

1. Text
2. Image
3. Audio
4. Video
5. Animation

4.1 Text

Text is the simplest multimedia component.

Examples:

• Titles
• Paragraphs
• Captions

4.2 Images

Two major categories:

Raster/Bitmap

Made up of pixels.

Examples:

• JPEG
• PNG
• GIF
• BMP

Vector

Based on geometric shapes/paths.

Examples:

• SVG

4.3 Audio

Important formats:

• MP3
• WAV
• AAC

Sampling

Digital audio is produced by sampling an analog signal.

Higher sampling rate generally means more samples per second.

4.4 Video

Video consists of a sequence of frames.

Common formats/codecs include:

• MP4
• H.264
• H.265/HEVC

4.5 Animation

Animation creates the appearance of movement by displaying images/objects in sequence.

Types include:

• 2D animation
• 3D animation

5. Internet of Things (IoT)

IoT = Internet of Things

IoT refers to a network of physical objects equipped with:

• Sensors
• Software
• Processing capability
• Connectivity

that enable them to collect and exchange data.

Examples:

• Smart watch
• Smart bulb
• Smart thermostat
• Smart agriculture system
• Connected vehicles

5.1 Basic IoT Architecture

A simplified IoT architecture can be understood as:

Sensors → Network → Processing/Cloud → Application

Sensors

Collect data.

Examples:

• Temperature
• Humidity
• Motion
• Light

Connectivity

Transfers data through technologies such as:

• Wi-Fi
• Bluetooth
• Cellular networks
• Zigbee
• LoRaWAN

Processing

Data may be processed:

• At the edge
• At a gateway
• In the cloud

Application

Provides useful services to users.

5.2 IoT Characteristics

• Connectivity
• Sensing
• Automation
• Data exchange
• Remote monitoring
• Intelligence
• Scalability

5.3 IoT Applications

Smart Home

• Smart lights
• Smart locks
• Smart appliances

Healthcare

• Wearable devices
• Remote patient monitoring

Agriculture

• Soil monitoring
• Smart irrigation
• Weather monitoring

Industry

• Predictive maintenance
• Machine monitoring

Smart City

• Smart traffic management
• Waste management
• Smart parking

5.4 IoT Challenges

Important challenges:

• Security
• Privacy
• Interoperability
• Scalability
• Power consumption
• Network reliability
• Device management

6. Artificial Intelligence

AI = Artificial Intelligence

AI is the field of computing concerned with creating systems capable of performing tasks that normally require aspects of human intelligence.

Examples:

• Learning
• Reasoning
• Problem solving
• Perception
• Language understanding
• Decision making

6.1 AI Applications

• Chatbots
• Recommendation systems
• Speech recognition
• Image recognition
• Medical diagnosis support
• Autonomous systems
• Fraud detection

6.2 Machine Learning

Machine Learning (ML) is a subfield of AI in which systems learn patterns from data.

Three major types:

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

6.3 Supervised Learning

The model learns from labeled data.

Examples:

• Classification
• Regression

Classification

Predicts categories.

Example:

Spam / Not Spam

Regression

Predicts numerical values.

Example:

House price prediction

6.4 Unsupervised Learning

Uses unlabeled data.

Common tasks:

• Clustering
• Dimensionality reduction

Example:

Grouping customers based on purchasing behavior.

6.5 Reinforcement Learning

An agent learns by interacting with an environment.

It receives:

• Rewards
• Penalties

Goal:

Maximize cumulative reward.

6.6 Neural Networks

Artificial Neural Networks are computational models inspired loosely by biological neural systems.

Basic components:

• Input layer
• Hidden layer(s)
• Output layer

Used for:

• Image recognition
• Speech recognition
• Classification
• Prediction

6.7 Deep Learning

Deep Learning uses neural networks with multiple layers.

It is a subset of:

Machine Learning → AI

Relationship:

AI ⟶ Machine Learning ⟶ Deep Learning

6.8 Natural Language Processing

NLP = Natural Language Processing

NLP enables computers to process and work with human language.

Examples:

• Chatbots
• Machine translation
• Sentiment analysis
• Speech assistants
• Text classification

6.9 Computer Vision

Computer Vision enables machines to interpret visual information.

Applications:

• Face recognition
• Object detection
• Medical image analysis
• Autonomous vehicles

6.10 Expert Systems

An expert system attempts to solve problems in a specialized domain using:

• Knowledge Base
• Inference Engine

Example:

Medical diagnosis expert system.

Bihar STET Last-Minute Revision