📘 PAPER 3: ARTIFICIAL INTELLIGENCE & ROBOTICS UNIT 1 – INTRODUCTION TO AI & SEARCH TECHNIQUES (university of allahabad)

 

🔴 UNIT 1 – INTRODUCTION TO AI & SEARCH TECHNIQUES

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1️⃣ Introduction to Artificial Intelligence (AI)

✅ Definition of AI

Artificial Intelligence is a branch of computer science that deals with creating intelligent machines capable of performing tasks that normally require human intelligence.

Examples:

• Speech recognition

• Face detection

• Self-driving cars

• Chatbots

• Robotics

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2️⃣ Scope of Artificial Intelligence

AI is widely used in the following fields:

🔹 1. Games

• Chess

• Ludo

• Video games

• AlphaGo

🔹 2. Theorem Proving

• Logical reasoning

• Mathematical proof verification

🔹 3. Natural Language Processing (NLP)

• Language translation

• Chatbots

• Speech recognition

🔹 4. Vision Systems

• Face recognition

• Object detection

• Medical image analysis

🔹 5. Robotics

• Industrial robots

• Medical robots

• Space robots

🔹 6. Expert Systems

• Medical diagnosis

• Fault detection

• Decision-making systems

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3️⃣ AI Techniques

AI techniques are methods used to solve complex problems.

Important AI Techniques:

• Search techniques

• Knowledge representation

• Heuristic methods

• Machine learning

• Reasoning techniques

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4️⃣ Intelligent Agents

✅ Definition

An Intelligent Agent is an entity that:

• Perceives environment using sensors

• Acts using actuators

• Makes decisions intelligently

---

Components of Intelligent Agent:

Component	Description
Sensors	Collect information
Actuators	Perform actions
Environment	Where agent operates
Agent function	Maps perception to action

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Types of Agents

1. Simple reflex agent

2. Model-based agent

3. Goal-based agent

4. Utility-based agent

5. Learning agent

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5️⃣ Search Techniques in AI

Search is the heart of AI.

Search Problem Components:

• Initial state

• Goal state

• Operators

• State space

• Path cost

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6️⃣ State Space Search

Definition:

State space is a set of all possible states from initial to goal state.

Example:

8-puzzle problem

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7️⃣ Control Strategies

Control strategy decides:

• Which node to expand next

• How to traverse state space

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8️⃣ Blind Search Techniques

🔹 1. Breadth First Search (BFS)

Characteristics:

• Explores level by level

• Uses Queue (FIFO)

• Finds shortest path

Advantages:

✔ Complete
✔ Optimal

Disadvantages:

❌ High memory usage
❌ Slow for large problems

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🔹 2. Depth First Search (DFS)

Characteristics:

• Goes deep first

• Uses Stack (LIFO)

Advantages:

✔ Less memory
✔ Easy to implement

Disadvantages:

❌ May go into infinite loop
❌ Not optimal

---

🔹 BFS vs DFS

BFS	DFS
Uses queue	Uses stack
Complete	Not always
High memory	Low memory
Optimal	Not optimal

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9️⃣ Heuristic Search Techniques

✅ Heuristic

A heuristic is a rule of thumb used to guide the search.

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🔹 1. Hill Climbing

• Moves toward better state

• Uses heuristic function

• Greedy approach

Problems:

• Local maxima

• Plateau

• Ridge

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🔹 2. Best First Search

• Uses evaluation function f(n)

• Chooses best node first

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🔹 3. A* Algorithm

Formula:

$$f(n) = g(n) + h(n)$$

Where:

• g(n) = cost from start to n

• h(n) = heuristic estimate

✔ Complete
✔ Optimal
✔ Widely used

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🔟 AND–OR Graphs & AO* Algorithm

AND–OR Graph

• OR node → choose one path

• AND node → all paths required

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AO* Algorithm

• Used for AND–OR graphs

• Improves A* algorithm

• Finds optimal solution graph

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1️⃣1️⃣ Constraint Satisfaction Problem (CSP)

Definition:

A problem defined by:

• Variables

• Domains

• Constraints

Example:

• N-Queens problem

• Map coloring

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1️⃣2️⃣ Game Playing in AI

Minimax Algorithm

• Two-player game

• MAX vs MIN

• Used in chess

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Alpha-Beta Pruning

• Optimization of minimax

• Reduces nodes explored

• Faster execution

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1️⃣3️⃣ Genetic Algorithms

Inspired by:

• Natural selection

• Evolution

Steps:

1. Initialization

2. Selection

3. Crossover

4. Mutation

5. New generation

📘 PAPER 2: OBJECT ORIENTED PROGRAMMING WITH PYTHON UNIT 5 – NumPy and Pandas (university of allahabad)

 

🔴 UNIT 5 – NumPy and Pandas

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🟦 PART A: NUMPY (Numerical Python)

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1️⃣ Introduction to NumPy

✅ What is NumPy?

NumPy is a Python library used for:

• Numerical computation

• Scientific computing

• Working with arrays and matrices

✅ Features of NumPy

✔ Faster than Python lists
✔ Supports multi-dimensional arrays
✔ Efficient memory usage
✔ Used in ML, AI, Data Science

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2️⃣ ndarray (N-Dimensional Array)

Definition:

The main object of NumPy is ndarray, which represents a multi-dimensional array.

Example:

import numpy as np
a = np.array([1,2,3])
print(a)

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3️⃣ Data Types in NumPy

a = np.array([1,2,3], dtype=float)

Common Data Types:

• int

• float

• bool

• complex

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4️⃣ Array Attributes

Attribute	Description
ndim	Number of dimensions
shape	Size of array
size	Total elements
dtype	Data type

Example:

a = np.array([[1,2],[3,4]])
print(a.ndim)
print(a.shape)

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5️⃣ Array Creation Routines

🔹 From List

np.array([1,2,3])

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🔹 Zeros & Ones

np.zeros((2,2))
np.ones((3,3))

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🔹 Using arange()

np.arange(1,10,2)

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🔹 Using linspace()

np.linspace(1,10,5)

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6️⃣ Array from Existing Data

np.asarray([1,2,3])
np.frombuffer(b'hello', dtype='S1')

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7️⃣ Array Indexing & Slicing

a = np.array([10,20,30,40])
print(a[1])
print(a[1:3])

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8️⃣ Mathematical Operations

a + b
a * b
np.sqrt(a)
np.sum(a)
np.mean(a)

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🟩 PART B: PANDAS

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9️⃣ Introduction to Pandas

✅ What is Pandas?

Pandas is a Python library used for:

• Data analysis

• Data manipulation

• Handling structured data

---

🔟 Pandas Data Structures

1️⃣ Series

A one-dimensional labeled array.

import pandas as pd
s = pd.Series([10,20,30])

---

2️⃣ DataFrame

A two-dimensional table-like structure.

data = {
    "Name": ["Amit", "Rahul"],
    "Marks": [80, 90]
}
df = pd.DataFrame(data)

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1️⃣1️⃣ Creating Series

From List

pd.Series([1,2,3])

From Dictionary

pd.Series({'a':10, 'b':20})

From Scalar

pd.Series(5, index=[1,2,3])

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1️⃣2️⃣ Creating DataFrame

From List

pd.DataFrame([[1,2],[3,4]])

From Dictionary

pd.DataFrame({
    "Name":["A","B"],
    "Age":[20,22]
})

---

1️⃣3️⃣ Manipulating DataFrames

Rename Column

df.rename(columns={"Name":"Student_Name"})

---

Delete Column

df.drop("Age", axis=1)

---

Delete Row

df.drop(0)

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1️⃣4️⃣ Handling Missing Values

Finding Missing Values

df.isnull()

---

Filling Missing Values

df.fillna(0)

---

Dropping Missing Values

df.dropna()

---

1️⃣5️⃣ Advantages of Pandas

✔ Easy data handling
✔ Fast processing
✔ Data cleaning
✔ Used in ML & AI

📘 PAPER 2: OBJECT ORIENTED PROGRAMMING WITH PYTHON UNIT 4 – Dictionaries, Functions, File Handling & Regular Expressions (university of allahabad)

 

🔴 UNIT 4 – Dictionaries, Functions, File Handling & Regular Expressions

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1️⃣ Dictionaries in Python

✅ Definition

A dictionary is an unordered collection of data stored as key–value pairs.

Example:

student = {
    "name": "Amit",
    "age": 20,
    "course": "MCA"
}

---

🔹 Characteristics

✔ Key-value based
✔ Mutable
✔ Keys must be unique
✔ Fast access

---

🔹 Accessing Dictionary Elements

print(student["name"])

---

🔹 Adding & Updating Values

student["age"] = 21
student["city"] = "Delhi"

---

🔹 Deleting Elements

del student["age"]

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2️⃣ Counting Frequency Using Dictionary

Example:

text = "banana"
freq = {}

for ch in text:
    if ch in freq:
        freq[ch] += 1
    else:
        freq[ch] = 1

print(freq)

Output:

{'b':1, 'a':3, 'n':2}

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3️⃣ Python Functions

✅ Definition

A function is a block of reusable code.

---

🔹 Function Syntax

def function_name(parameters):
    statements
    return value

---

🔹 Types of Arguments

1. Positional

2. Keyword

3. Default

4. Variable-length

---

Example:

def add(a, b=5):
    return a + b

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4️⃣ Passing Function as Argument

def square(x):
    return x*x

def fun(f, value):
    return f(value)

print(fun(square, 5))

---

5️⃣ Lambda Function

Definition:

Anonymous function written in one line.

square = lambda x: x*x
print(square(5))

---

6️⃣ Map Function

nums = [1,2,3]
result = list(map(lambda x: x*2, nums))

---

7️⃣ List Comprehension

squares = [x*x for x in range(5)]

---

8️⃣ File Handling in Python

---

🔹 Opening a File

f = open("data.txt", "r")

File Modes:

Mode	Meaning
r	Read
w	Write
a	Append
r+	Read + Write

---

🔹 Reading File

f.read()

---

🔹 Writing File

f.write("Hello Python")

---

🔹 Closing File

f.close()

---

9️⃣ String Processing

Common String Functions:

s.upper()
s.lower()
s.replace()
s.split()
s.strip()

---

🔟 Regular Expressions (RegEx)

Definition:

Regular expressions are used for pattern matching.

---

Common Symbols

Symbol	Meaning
.	Any character
^	Start of string
$	End of string
*	Zero or more
+	One or more
[a-z]	Range

---

Example:

import re
pattern = re.search("python", "I love python")

📘 PAPER 2: OBJECT ORIENTED PROGRAMMING WITH PYTHON UNIT 3 – Classes, Objects & OOP Concepts (university of allahabad)

 

🔴 UNIT 3 – Classes, Objects & OOP Concepts

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1️⃣ Class and Object

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✅ Class

A class is a blueprint or template used to create objects.

📌 It defines:

• Data members (variables)

• Member functions (methods)

Example:

class Student:
    def show(self):
        print("This is a student")

---

✅ Object

An object is an instance of a class.

s1 = Student()
s1.show()

---

🔹 Key Difference

Class	Object
Blueprint	Real-world entity
Logical	Physical
No memory	Uses memory

---

2️⃣ Abstract Data Type (ADT)

✅ Definition

An ADT defines:

• What operations are to be performed

• Not how they are implemented

It focuses on behavior, not implementation.

Example:

• Stack

• Queue

• List

---

3️⃣ Classes and Objects in Python

Example Program:

class Person:
    def __init__(self, name):
        self.name = name

    def display(self):
        print("Name:", self.name)

p = Person("Rahul")
p.display()

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4️⃣ Constructor (__init__)

Definition:

A constructor is a special method used to initialize objects.

Example:

class Student:
    def __init__(self, roll):
        self.roll = roll

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5️⃣ Features of OOP in Python

---

🔹 1. Encapsulation

Binding data and methods together.

class Bank:
    def __init__(self):
        self.__balance = 5000

✔ Data hiding
✔ Security

---

🔹 2. Abstraction

Showing essential features and hiding internal details.

Achieved using:

• Abstract classes

• Interfaces

---

🔹 3. Inheritance

Child class inherits properties of parent class.

class Parent:
    def show(self):
        print("Parent")

class Child(Parent):
    pass

---

Types of Inheritance:

1. Single

2. Multiple

3. Multilevel

4. Hierarchical

5. Hybrid

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🔹 4. Polymorphism

Same function behaves differently.

Example:

print(len("Python"))
print(len([1,2,3]))

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6️⃣ Abstract Class

Definition:

An abstract class contains abstract methods (methods without body).

Using abc module:

from abc import ABC, abstractmethod

class Shape(ABC):
    @abstractmethod
    def area(self):
        pass

---

7️⃣ Scope in Python

Types of Scope:

1. Local

2. Global

3. Non-local

4. Built-in

---

Example:

x = 10  # global

def fun():
    x = 5  # local
    print(x)

---

8️⃣ Multithreading in Python

---

✅ Definition

Multithreading means executing multiple threads simultaneously.

---

Is Multithreading Supported in Python?

✔ Yes
❌ But limited due to GIL (Global Interpreter Lock)

---

Example:

import threading

def display():
    print("Hello")

t = threading.Thread(target=display)
t.start()

---

Advantages:

• Faster execution

• Better CPU utilization

• Useful in I/O operations

---

Disadvantages:

• Complex debugging

• GIL limits performance

📘 PAPER 2: OBJECT ORIENTED PROGRAMMING WITH PYTHON UNIT 2 – Python Data Structures (university of allahabad)

 

🔴 UNIT 2 – Python Data Structures

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1️⃣ Introduction to Python Data Structures

✅ What is a Data Structure?

A data structure is a way to store, organize, and manage data efficiently so that operations can be performed easily.

Python provides built-in data structures, such as:

• List

• Tuple

• Set

• Dictionary

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2️⃣ Python Data Types (Revisited)

🔹 Mutable Data Types

Data that can be changed after creation.

• List

• Dictionary

• Set

🔹 Immutable Data Types

Data that cannot be changed.

• Integer

• Float

• String

• Tuple

---

3️⃣ Python Lists

✅ Definition

A list is an ordered, mutable collection of elements.

Example:

numbers = [10, 20, 30, 40]

---

🔹 List Characteristics

✔ Ordered
✔ Mutable
✔ Allows duplicate values
✔ Indexed

---

🔹 List Operations

Accessing elements

print(numbers[0])

Slicing

numbers[1:3]

Adding elements

numbers.append(50)

Removing elements

numbers.remove(20)

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4️⃣ List Slicing

Syntax:

list[start : end : step]

Example:

a = [1,2,3,4,5]
print(a[1:4])

Output:

[2, 3, 4]

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5️⃣ Indexing

• Positive indexing → Left to right

• Negative indexing → Right to left

a = [10, 20, 30]
print(a[-1])   # 30

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6️⃣ List Concatenation

a = [1,2]
b = [3,4]
c = a + b

Output:

[1,2,3,4]

---

7️⃣ Searching in Python

🔹 Linear Search

Searches element one by one.

def linear_search(lst, key):
    for i in lst:
        if i == key:
            return True
    return False

✔ Simple
❌ Slow for large data

---

🔹 Binary Search

Works only on sorted lists

def binary_search(arr, key):
    low = 0
    high = len(arr)-1
    while low <= high:
        mid = (low+high)//2
        if arr[mid] == key:
            return True
        elif arr[mid] < key:
            low = mid+1
        else:
            high = mid-1
    return False

✔ Fast
✔ Efficient

---

8️⃣ Inductive Function Definition

Meaning:

A function defined using:

1. Base case

2. Recursive case

Example:

Factorial

def fact(n):
    if n == 0:
        return 1
    return n * fact(n-1)

---

9️⃣ Sorting Techniques

🔹 Selection Sort

Steps:

1. Find minimum

2. Swap with first element

3. Repeat

def selection_sort(arr):
    for i in range(len(arr)):
        min = i
        for j in range(i+1, len(arr)):
            if arr[j] < arr[min]:
                min = j
        arr[i], arr[min] = arr[min], arr[i]

---

🔹 Insertion Sort

def insertion_sort(arr):
    for i in range(1, len(arr)):
        key = arr[i]
        j = i - 1
        while j >= 0 and key < arr[j]:
            arr[j+1] = arr[j]
            j -= 1
        arr[j+1] = key

---

🔟 In-Place Sorting

Definition:

Sorting without using extra memory.

✔ Selection Sort
✔ Insertion Sort

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1️⃣1️⃣ Tuples

Definition:

Tuple is an ordered but immutable collection.

t = (10, 20, 30)

✔ Faster than list
✔ Used for fixed data

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1️⃣2️⃣ Mutability Concept

Data Type	Mutable
List	Yes
Tuple	No
String	No
Dictionary	Yes

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1️⃣3️⃣ Programs Using Lists

Find Maximum Element

lst = [4, 8, 2, 9]
print(max(lst))

Find Minimum Element

print(min(lst))

Find Mean

mean = sum(lst) / len(lst)