INTERNSHIP REPORT

A report submitted in partial fulfillment of there requirements for the Award of Degree
of

BACHELOR OF TECHNOLOGY
in
INTERNET OF THINGS
by
CHAGAMREDDY SARATH REDDY
Regd.No.:21481A6010

Under Supervision of
Mr. R. Upendra Rao, Director
BIST Technologies
(Duration:13th May,2024 to 6th july, 2024)

DEPARTMENT OF INTERNET OF THINGS

SESHADRI RAO GUDLAVALLERU ENGINEERING COLLEGE
(An Autonomous Institute with Permanent Affiliation To JNTUK, Kakinada)
Seshadri Rao Knowledge Village, Gudlavalleru, Krishna (Dt),
Andhra Pradesh - 521356.

2021 – 2025
 SESHADRI RAO GUDLAVALLERU ENGINEERING COLLEGE
(An Autonomous Institute with Permanent Affiliation to JNTUK, Kakinada)

SESHADRI RAO KNOWLEDGE VILLAGE, GUDLAVALLERU

DEPARTMENT OF INTERNET OF THINGS

CERTIFICATE

This is to certify that the project report entitled “PYTHON PROGRAMMING”

is a bonafide record of work carried out by CHAGAMREDDY SARATH REDDY
(21481A6010) as a part of internship in thepartial fulfillment of the
requirements for the award of the Degree of Bachelor of Technology in
Internet of Things of Jawaharlal Nehru Technological University Kakinada,
Kakinada during the academic year 2024-2025.

Guide Head of the Department

(Sk.Bajivali) (Dr.Y.Syamala)
 ACKNOWLEDGEMENT

I am glad to express my deep sense of gratitude to BIST Technologies for

their guidance and cooperation in completing this internship. Through this,
I want to convey my sincere thanks to them for inspiring assistance during
my internship.
It is indeed with a great sense of pleasure and immense sense of gratitude that I
acknowledge the help of these individuals.

I express my heartful gratitude and deep in debt to our beloved Head of the
Department Dr. Y.Syamala for her great help and encouragement in doing my
internship successfully.

I also express my gratitude to our principal Dr. B. Karuna Kumar for his
encouragement and facilities provided during the course of internship.

I express my heartful gratitude to all Faculty Members, all Lab Technicians, who
helped me in all aspects of lab work. I thank one and all who rendered help to me
directly or indirectly in the completion of this internship.

CHAGAMREDDY SARATH REDDY

 ABSTRACT

Python programming is a versatile and widely used skill that has the potential
to revolutionize countless industries. This report provides a comprehensive
overview of Python, exploring its core concepts, applications, and best
practices.
At the heart of Python lies the development of efficient, readable, and
maintainable code capable of solving diverse problems. We delve into
various Python techniques, including object-oriented programming, data
analysis, and web development, which empower developers to create robust
solutions.
The applications of Python are vast and far-reaching. In healthcare, Python-
powered tools assist in data analysis, automation, and simulation of medical
processes. In education, Python-based platforms enhance coding education
and interactive learning experiences. In agriculture, Python optimizes data
collection, analyzes soil health, and predicts climate impacts. In engineering,
Python aids in computational analysis, automation, and system modeling.
However, the use of Python in critical applications also raises significant
concerns. Issues such as code security, maintainability, and scalability must
be carefully addressed to ensure Python is utilized effectively and
responsibly. It is crucial to adopt guidelines and standards to govern the
development of Python projects, mitigating potential risks and maximizing
benefits.
 INDEX
TITLE PAGE NO
1. INTRODUCTION 9
1.1 HISTORY OF AI 10
1.2 TYPES OF AI 11

2. APPLICATIONS OF AI 12
15
3. AGENTS IN AI
17
4. PROBLEM-SOLVING IN AI: SEARCH ALGORITHMS
4.1 UNINFORMED SEARCH

4.2 INFORMED SEARCH

5.KNOWLEDGE REPRESENTATION 20
5.1 TECHNIQUES
6.PROPOSITIONAL LOGIC 22

7.FIRST ORDER LOGIC 24

8.CHAINING 25
26
9.REASIONING
27
10.PROBABILISTIC REASONING IN AI
11.BAYES THEOREM 28
29
12.DECISION TREE
30
13.OVERVIEW CONTENTS IN AI
14.PROGRAMME OUTCOMES
 LIST OF FIGUERS
Figure No. Name of Figure

1.1 Artificial Intelligence Introduction

1.1.1 History of Artificial Intelligence

1.2.1 Types of Artificial Intelligence

2.1 Applications of Artificial Intelligence

3.1 Types of Agents in Artificial Intelligence

4.1.1 Types of Agents Uninformed Search

4.2.1 Types of Agents Informed Search

5.1.1 Knowledge Representation Techniques

8.1 Knowledge Representation Techniques

9.1 Reasoning types in AI

10.1 Formula of probability occurrence

11.1 Formula Terms Explanation of Bayes Theorem

12.1 Decision Tree

 Learning Objectives/Internship Objectives

➢ Internships are generally thought of to be reserved for college students looking to

gain experience in a particular field. However, a wide array of people can benefit
from Training Internships in order to receive real world experience and develop their
skills.
➢ An objective for this position should emphasize the skills you already possess in the area and
your interest in learning more.
➢ Internships are utilized in a number of different career fields, including architecture,
engineering, healthcare, economics, advertising and many more.

➢ Some internship is used to allow individuals to perform scientific research while others are
specifically designed to allow people to gain first-hand experience working.

➢ Utilizing internships is a great way to build your resume and develop skills that can
be emphasized in your resume for future jobs. When you are applying for a Training
Internship, make sure to highlight any special skills or talents that can make you
stand apart from the rest of the applicants so that you have an improved chance of
landing the position
WEEKLY OVERVIEW OF INTERNSHIP ACTIVITIES

WEEK 1:13-05-2024 TO 18-05-2024

Introduction to Artificial Intelligence
➢ History of AI
➢ Types of AI

WEEK 2:20-05-2024 TO 25-05-2024

Applications of AI
Agents in AI

WEEK 3:27-05-2024 TO 01-06-2024

Problem-solving in AI: Search Algorithms
➢ Uninformed Search
➢ Informed Search

WEEK 4: 03-06-2024 TO 08-06-2024

Knowledge Representation
➢ Techniques

WEEK 5:10-06-2024 TO 15-06-2024

Propositional Logic
First Order Logic

WEEK 6: 17-06-2024 TO 22-06-2024

Chaining
Reasoning

WEEK 7: 24-06-2024 TO 29-06-2024

Probabilistic Reasoning in AI
Baye’s Theorem

WEEK 8: 01-07-2024 TO 06-07-2024

Decision Tree
Overview of Contents in AI

8
1.INTRODUCTION

Fig 1.1. Artificial Intelligence Introduction

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines

programmed to think, learn, and act like humans. AI systems mimic cognitive functions
such as learning, reasoning, problem-solving, and decision-making to perform tasks that
typically require human intelligence. The core goal of AI is to build systems that can
perceive, comprehend, and act in a way that benefits society, businesses, and individuals.

AI has become a transformative technology across industries, enhancing automation,

decision-making, and problem-solving capabilities. It relies on techniques like machine
learning, deep learning, natural language processing (NLP), and computer vision to
process vast amounts of data, recognize patterns, and generate insights.
In recent years, advancements in computational power, big data, and algorithms have
enabled AI to achieve significant milestones. From virtual assistants like Siri and Alexa
to autonomous vehicles and intelligent robotics, AI is now a critical component of
modern technological progress.

9
1.1 HISTORY OF AI

Fig 1.1.1. History of Artificial Intelligence

The concept of AI dates back to ancient myths and early philosophies, but the formal
study of AI began in the mid-20th century. Key milestones in the history of AI include:
• Ancient Period: Philosophers like Aristotle laid the foundation for logic and
reasoning systems, which would later inspire AI.
• 1943: McCulloch and Pitts developed the first model of artificial neurons, laying
the groundwork for neural networks.
• 1950: Alan Turing published the paper "Computing Machinery and Intelligence"
and proposed the Turing Test to evaluate machine intelligence.
• 1956: The term "Artificial Intelligence" was coined by John McCarthy at the
Dartmouth Conference, marking the formal birth of AI as a field.
• 1960s-1970s: Early AI research focused on symbolic systems and problem-solving
techniques, leading to basic AI programs. However, limitations in computing
power caused setbacks, often referred to as the AI Winter.
• 1980s: The introduction of expert systems revolutionized AI, allowing machines
to solve domain-specific problems using rule-based approaches.
• 1990s-2000s: The development of machine learning and statistical models enabled
AI to analyze and learn from data more efficiently.
• 2010-Present: AI witnessed exponential growth with the advent of deep learning,
big data, and improved hardware like GPUs. Notable achievements include
Google's AlphaGo, breakthroughs in natural language processing (e.g., GPT
models), and advancements in autonomous systems.

10
1.2 TYPES OF AI

Fig 1.2.1. Types of Artificial Intelligence

AI can be categorized into different types based on capabilities and functionalities:

1. Based on Capabilities:
o Narrow AI: Also known as weak AI, it is designed to perform specific
tasks. Examples include virtual assistants (e.g., Siri, Alexa),
recommendation systems, and image recognition tools.
o General AI: Also known as strong AI, it is a hypothetical form of AI
capable of performing any intellectual task a human can do. General AI can
reason, learn, and adapt across multiple domains.
o Super AI: This refers to a futuristic AI that surpasses human intelligence in
all aspects, including creativity, emotional intelligence, and decision-
making.
2. Based on Functionalities:
o Reactive Machines: These systems act based on current inputs without
using past experiences. Example: IBM's Deep Blue chess computer.
o Limited Memory: These systems use past data to make decisions.
Examples include self-driving cars that analyze traffic data and
surroundings.
o Theory of Mind: A hypothetical AI that understands human emotions,
beliefs, and intentions to interact more effectively.
o Self-Aware AI: This advanced form of AI would have self-awareness and
consciousness, a concept still in the theoretical stage.

11
2.APPLICATIONS OF AI
AI is revolutionizing various sectors by enhancing efficiency and enabling innovation:

Fig 2.1. Applications of Artificial Intelligence

1. Healthcare

o Diagnostics: AI is used to analyze medical data, such as images (X-rays,

MRIs) or genetic data, to assist in diagnosing diseases like cancer, heart
disease, and neurological disorders.
o Personalized Medicine: AI helps create personalized treatment plans based
on a patient's individual characteristics and medical history.

12
2. Finance

o Algorithmic Trading: AI is used to develop algorithms that predict stock

market trends and make high-frequency trades.
o Fraud Detection: AI systems can detect unusual patterns in financial
transactions, helping to prevent fraud.

3. Transportation

o Autonomous Vehicles: Self-driving cars and drones rely on AI to navigate,

detect obstacles, and make decisions in real-time.
o Traffic Management: AI systems optimize traffic flow by analyzing real-
time data and adjusting traffic signals to reduce congestion.

4. Retail

o Recommendation Systems: AI algorithms analyze consumer behavior and

preferences to suggest products, enhancing the shopping experience.
o Inventory Management: AI helps retailers predict demand and optimize
stock levels to avoid overstocking or stockouts.

5. Manufacturing

o Predictive Maintenance: AI systems predict when machines or equipment

are likely to fail, enabling timely maintenance and reducing downtime.
o Quality Control: AI is used to inspect products on production lines,
identifying defects and ensuring quality standards.

6. Education

o Personalized Learning: AI systems tailor educational content to individual

students’ learning styles, pacing, and progress.
o Automated Grading: AI can help grade assignments and exams, providing
instant feedback to students.

7. Entertainment

o Content Recommendation: Streaming platforms like Netflix and Spotify

use AI to recommend movies, TV shows, and music based on user
preferences.

Game Development: AI is used in video games to create realistic behaviors for

non-playable characters (NPCs) and to design dynamic game environments.
13
8. Natural Language Processing (NLP)

o Speech Recognition: AI-powered speech recognition systems are used in

virtual assistants (like Siri and Alexa), transcription services, and
customer support. Machine Translation: AI-based translation systems
o like Google Translate are improving in accuracy, enabling real-time
translation across languages.

9. Security

o Surveillance: AI is used in facial recognition and behavior analysis to

enhance security in public spaces and private establishments.
o Cybersecurity: AI systems detect and respond to cyber threats in real-time,
identifying anomalies and potential vulnerabilities.

10. Agriculture

o Precision Farming: AI helps farmers optimize crop yields by analyzing soil

conditions, weather patterns, and crop health.
o Pest and Disease Detection:AI systems can identify pests or diseases in
crops through image recognition and suggest treatment options.

11. Customer Service

o AI Chatbots: Many companies use AI-driven chatbots to handle customer

inquiries, providing fast and accurate responses.
o Virtual Assistants: AI-based assistants help customers with scheduling,
reminders, and information retrieval.

12. Smart Cities

o Energy Management: AI helps optimize energy consumption in cities by

managing power grids and predicting demand.
o Waste Management: AI is used to optimize waste collection routes and
predict when bins need to be emptied.

13. Legal

o Contract Review: AI tools can review and analyze legal documents,

identifying key clauses, risks, and discrepancies.
o Legal Research: AI systems assist lawyers in finding relevant case law and
legal precedents.
14
3.AGENTS IN AI
Agents in AI are entities that perceive their environment through sensors and act
upon that environment using actuators. An agent operates autonomously to achieve
specific goals by processing inputs (perceptions) and taking actions based on its
internal model or programmed logic.

Fig 3.1. Types of Agents in Artificial Intelligence

Types of Agents in AI:

1. Simple Reflex Agents:

o These agents act based only on the current percept, ignoring past states.
o They use condition-action rules, such as "if condition, then action."
o Example: A thermostat that turns on heating if the temperature drops below
a certain threshold.
2. Model-Based Reflex Agents :
o These agents maintain an internal state that depends on past perceptions.
o The internal model helps the agent track the current state of the
environment.
o Example: A self-driving car tracking the position of nearby vehicles.
3. Goal-Based Agents:
o These agents act to achieve specific goals.

15
 o They use goal information to evaluate which actions will move them closer
to the goal.
o Example: GPS navigation systems finding the shortest route to a destination.
4. Utility-Based Agents:
o These agents aim to maximize their overall performance by evaluating
different outcomes using a utility function.
o Example: An AI trading system optimizing profits in the stock market.
5. Learning Agents:
These agents
o improve their performance over time by learning from their
experiences.
They useofeedback mechanisms (rewards or penalties) to update their
behavior.
Example:o AI systems playing games like AlphaGo, which improve with
practice.
Agent Components:

• Sensors: Devices or inputs used to perceive the environment (e.g., cameras,

microphones).
• Actuators: Components that perform actions to affect the environment (e.g., robot
arms, speakers).
• Agent Function: Maps percepts to actions, defining the behavior of the agent.
•
Performance Measure: Evaluates how well the agent achieves its goals.
Agents form the backbone of AI systems, enabling them to interact with the real world
and make decisions based on data and learned behaviors. They are widely used in
robotics, game AI, autonomous vehicles, and intelligent assistants.

16
4.PROBLEM-SOLVING IN AI: SEARCH ALGORITHMS

Search algorithms are fundamental techniques in AI used to solve problems by exploring

possible solutions systematically. They help AI systems make decisions, find optimal
solutions, and navigate complex environments.

4.1 UNINFORMED SEARCH

Uninformed search (or blind search) algorithms explore the search space without any prior
knowledge of the goal state or the path to the solution. They rely solely on the structure of
the problem and operate systematically.

Fig 4.1.1. Types of Agents Uninformed Search

17
Types of Uninformed Search:

1. Breadth-First Search (BFS) :

o Explores all nodes at the current level before moving to the next level.
o Guarantees the shortest path for unweighted graphs.
o Example: Finding the shortest path in a maze.
2. Depth-First Search (DFS):
o Explores as deep as possible in one branch before backtracking.
o More memory-efficient but may not find the optimal solution.
o Example: Solving puzzles like mazes or navigating decision trees.
3. Uniform-Cost Search:
o Expands the least-cost node first using a cost function.
o Guarantees the least-cost solution.
o Example: Finding the cheapest route in logistics.
4. Depth-Limited Search:
o DFS with a specified depth limit to prevent infinite loops.
5. Iterative Deepening Depth-First Search (IDDFS):
o Combines BFS and DFS by gradually increasing the depth limit.
o Example: Searching for solutions in large graphs.

Uninformed searches are simple and generic but may be inefficient for large or complex
problems.
4.2 INFORMED SEARCH

Informed search algorithms use problem-specific knowledge (heuristics) to guide the

search toward the goal state more efficiently. These algorithms aim to reduce the search
space and find solutions faster.

Types of Informed Search:

1. Greedy Best-First Search:

o Selects the node that appears closest to the goal based on a heuristic function.
o Faster but does not guarantee the optimal solution.
o Example: Navigating a map using straight-line distance to the destination.

18
 Fig 4.2.1. Types of Agents Informed Search

2.A* Search:
o Combines Uniform-Cost Search and Greedy Best-First Search.
o Uses the formula: f(n) = g(n) + h(n), where:
▪ g(n): Cost from the start node to the current node.
▪ h(n): Heuristic estimate from the current node to the goal.
o Guarantees the optimal solution if the heuristic is admissible.
o Example: Route optimization in GPS systems.
3. Hill Climbing:
o Iteratively moves toward the neighbor with the highest heuristic value.
o May get stuck in local maxima.
4. Beam Search:
o Examines only a fixed number of best candidates at each level.
5. Heuristic Search:
o Relies on domain-specific heuristics to estimate the best path to the goal.

Informed search algorithms improve efficiency by leveraging additional information about

the problem, making them suitable for solving complex AI tasks like pathfinding, game
playing, and optimization problems.

19
5.KNOWLEDGE REPRESENTATION
Knowledge Representation is a key area in Artificial Intelligence that focuses on how to
represent real-world knowledge in a structured format that a machine can use to perform
tasks such as reasoning, decision-making, and learning. The goal is to enable machines to
simulate human-like understanding and problem-solving by using a formal system of
knowledge representation.
Importance of Knowledge Representation

• It is essential for building intelligent systems that can reason about the world, make
decisions, and solve complex problems.
• It helps in natural language processing (NLP), computer vision, robotics, expert
systems, and more.

5.1 Knowledge Representation Techniques

There are several techniques for representing knowledge in AI systems, each suited to
different tasks:

Fig 5.1.1. Knowledge Representation Techniques

20
1.Logic-Based Representation:

• Propositional Logic: Represents knowledge using statements that are either true or
false.
• First-Order Logic (FOL) : Extends propositional logic to include variables,
predicates, and quantifiers (e.g., "For all x, if x is a human, then x is mortal").

2.Semantic Networks:

• A graph-based representation where nodes represent concepts and edges represent

relationships between those concepts.
• Often used in natural language processing and conceptual understanding.

3.Frames Representation:

•A frame is a data structure that holds specific pieces of information about an object
or concept. It's similar to an object in object-oriented programming.
• Frames can contain attributes and values, representing real-world objects or concept
(e.g., "Dog" might have attributes like "Breed" and "Size").

4.Production Rules:

• Knowledge is represented as a set of rules (if-then statements).

• Commonly used in expert systems where reasoning is based on specific conditions
or facts.

21
6.PROPOSITIONAL LOGIC
Propositional Logic (also known as Sentential Logic or Boolean Logic) is a formal
system used to represent statements or propositions that are either true or false. It is a
subset of logic and serves as a foundational concept in various areas of Artificial
Intelligence (AI), especially in knowledge representation and reasoning.
1. Basic Elements of Propositional Logic
In propositional logic, the basic unit of knowledge is a proposition, which is a
declarative statement that can be either true or false. These are typically represented by
propositional variables (often denoted as p,q,r,…)
For example:

• p: "The sky is blue."

• q: "It is raining."

2. Connectives in Propositional Logic

Propositional logic uses logical connectives to combine or manipulate propositions.
These connectives define the relationships between propositions and form the foundation
of logical reasoning. Some common logical connectives are:
1.AND(^)

•The conjunction operator (AND) is used when both propositions must be true.
• Symbol: p ∧ q
• Meaning: "p AND q", true only if both p and q are true.
• Example: "It is sunny AND warm."

2.OR(V)

• The disjunction operator (OR) is used when at least one of the propositions is true.
• Symbol: p ∨ q
• Meaning: "p OR q", true if either p or q is true.
• Example: "It is sunny OR cloudy."

3.NOT(¬)

• The negation operator (NOT) inverts the truth value of a proposition.

• Symbol: ¬p
22
 • Meaning: "NOT p", true if p is false, and false if p is true.
• Example: "It is NOT raining."

4.IMPLICATION(→ )
• The implication operator is used to express logical consequence, i.e., if one
proposition implies another.
• Symbol: p→q
• Meaning: "If p, then q", true unless p is true and q is false.
• Example: "If it is raining, then the ground is wet."

5.BIDIRECTIONAL(↔ )
• The biconditional operator expresses that two propositions are logically
equivalent, i.e., both must be either true or false.
• Symbol: p↔qp \leftrightarrow qp↔q
• Meaning: "p if and only if q", true if both ppp and qqq have the same truth value.
• Example: "You will get an A if and only if you pass the exam."

3. Tautology and Contradiction

• Tautology: A logical expression that is always true, regardless of the truth values
of its components.
o Example: p ∨ ¬p (Either p is true, or p is not true.)
• Contradiction: A logical expression that is always false, regardless of the truth
values of its components.
o Example: p ∧ ¬p (p cannot be both true and false.)

4.Applications of Propositional Logic

Propositional logic is widely used in various areas of AI, including:

• Knowledge Representation: Representing facts and knowledge about the world in

a structured, logical format.
• Automated Theorem Proving: Propositional logic is used in proving the validity
of statements or deriving conclusions from known facts.
• AI Planning: Planning systems use propositional logic to represent actions and
their consequences.
• Expert Systems: Representing rules and facts in expert systems, where
conclusions are drawn based on predefined conditions.
• Natural Language Processing (NLP): Used for parsing and understanding the
logical structure of sentences.
23
7. FIRST-ORDER LOGIC (FOL)
Definition: First-Order Logic (FOL) is an extension of propositional logic that allows for
the expression of relationships between objects using predicates, quantifiers, and
variables. Unlike propositional logic, which deals with simple true or false statements,
FOL can describe more complex structures involving objects and their relationships.
Key Components:

• Predicates: Represent properties or relations about objects. For example,

IsHuman(John) could mean "John is a human".
• Quantifiers:
o Universal Quantifier (∀): Expresses that a statement holds for all members
of a domain (e.g., ∀x (Human(x) → Mortal(x)) means "All humans are
mortal").
o Existential Quantifier (∃): Indicates that there exists at least one member
of the domain for which the statement is true (e.g., ∃x (HasPet(x)) means
"There exists someone who has a pet").
• Variables: Represent any object in the domain.

Applications:

• Used extensively in knowledge representation, natural language processing, and

automated theorem proving.
• Expert Systems and semantic web technologies rely on FOL for reasoning about
facts and relations.

24
8.CHAINING
Definition: Chaining refers to a technique used in AI for reasoning and drawing
conclusions. It involves using a series of logical steps to connect facts or rules, allowing
for the inference of new knowledge.

Fig 8.1. Knowledge Representation Techniques

Types of Chaining:

• Forward Chaining: A data-driven approach where you start from known facts and
use them to infer new facts until the goal is reached. This is used in rule-based
systems.
o Example: "If it is raining, the ground is wet. It is raining, so the ground is
wet."
• Backward Chaining: A goal-driven approach where you start from a goal or
hypothesis and work backward to see what facts or rules are required to support it.
o Example: "I want to know if the ground is wet. Is it raining? If yes, the
ground will be wet."

25
9. REASONING IN AI
Definition: Reasoning is the process of deriving conclusions from known facts and rules.
It allows AI systems to simulate logical thinking and decision-making processes.
Types of Reasoning:

Fig 9.1. Reasoning types in AI

• Deductive Reasoning: Drawing specific conclusions from general rules (e.g., All
humans are mortal; Socrates is human; therefore, Socrates is mortal).

• Inductive Reasoning: Generalizing from specific instances (e.g., Observing that

the sun rises every day and concluding that it will rise tomorrow)

• Abductive Reasoning: Inferring the best possible explanation from available

evidence (e.g., Seeing smoke and concluding there is a fire).

26
10. PROBABILISTIC REASONING IN AI
Definition: Probabilistic reasoning is used when AI systems need to make decisions
under uncertainty. It uses probability theory to represent and reason about uncertain
knowledge.

• Probability: Probability can be defined as a chance that an uncertain event will

occur. It is the numerical measure of the likelihood that an event will occur. The
value of probability always remains between 0 and 1 that represent ideal
uncertainties.

Fig 10.1. Formula of probability occurrence

Techniques:

• Bayesian Networks: A graphical model that represents probabilistic relationships

among a set of variables.
(MDPs): Used for modeling decision-making in
• Markov Decision Processes
environments with uncertainty.
• Monte Carlo Methods : Statistical methods used to make predictions based on
random sampling.

Applications:

• Machine learning models likeNaive Bayesand Gaussian Mixture Models use

probabilistic reasoning.
• Robotics for navigating uncertain environments.
•
Natural Language Processing(NLP) and speech recognitionrely on
probabilistic models.

27
11. BAYES THEOREM
Definition: Bayes’ Theorem provides a way to update the probability of a hypothesis
based on new evidence. It is fundamental in probabilistic reasoning and has
applications in many AI subfields.
Formula:

Fig 11.1. Formula Terms Explanation of Bayes Theorem

Applications:

• Spam filtering, where Bayes' theorem helps determine the probability that an
email is spam based on features like subject, sender, etc.
• Medical diagnosis systems use Bayesian reasoning to compute the likelihood of
various diseases given symptoms.
• Machine learning models like Naive Bayes classifier.

28
12. DECISION TREE
Definition: A Decision Tree is a tree-like model used for decision-making and
classification. Each internal node represents a feature or decision based on an attribute,
and each leaf node represents an outcome or classification.
Structure:

• Root Node: The feature used to split the data.

• Branches: The possible outcomes of a decision based on the feature.
• Leaf Nodes: The final decision or classification.

Fig 12.1. Decision Tree

Applications:

• Widely used in supervised machine learning for classification and regression

tasks.
• Algorithms like ID3, C4.5, and CART use decision trees for learning from labeled
data.
• Decision trees are easy to interpret and visualize, making them popular for both
research and application in various industries.

29
13. OVERVIEW OF AI
Artificial Intelligence (AI) is a broad field of computer science focused on creating
systems that can perform tasks that typically require human intelligence. These tasks
include reasoning, learning from experience, problem-solving, understanding natural
language, and perceiving the environment. AI seeks to build machines that can mimic
cognitive functions such as learning, decision-making, and understanding, enabling them
to autonomously perform tasks or assist humans in complex decision-making processes.
AI encompasses various subfields, each targeting different aspects of intelligence.
Machine Learning (ML), one of the most prominent subfields, involves creating
algorithms that allow computers to learn from data and improve their performance over
time without explicit programming. In Natural Language Processing (NLP), AI
systems are developed to understand, interpret, and generate human language, which is
crucial for applications like chatbots, translation services, and voice assistants.
Computer Vision allows machines to interpret and understand the visual world, enabling
tasks like image recognition, facial recognition, and object tracking. Another essential
area of AI is Robotics, where intelligent robots are designed to interact with the physical
world and perform tasks autonomously, such as in manufacturing, medical surgery, and
delivery systems.

AI also includes Expert Systems, which are designed to emulate human expertise in
specific domains by reasoning through knowledge bases to make decisions.
Reinforcement Learning, a part of ML, teaches machines to make a series of decisions
by interacting with their environment and learning from the consequences of their
actions, similar to how humans learn through trial and error.

The applications of AI are vast and varied, extending to industries like healthcare, where
AI is used for medical image analysis, personalized treatment plans, and drug discovery.
In the automotive sector, AI plays a key role in the development of autonomous vehicles,
helping them navigate and make decisions in real-time. In finance, AI-driven systems are
employed for tasks such as fraud detection, credit scoring, and algorithmic trading.
Moreover, AI has revolutionized the entertainment industry, with recommendation
algorithms in platforms like Netflix and YouTube, as well as in video game design,
where AI opponents react to players’ actions in realistic ways.
Despite the rapid progress, AI faces challenges such as ethical concerns, including bias in
algorithms, data privacy issues, and the impact of automation on jobs. These challenges
have led to growing interest in ensuring AI systems are developed responsibly, with
attention to fairness, transparency, and accountability. As AI continues to evolve, it holds
the potential to transform industries, improve efficiency, and address complex global
challenges, making it a crucial area of research and development for the future.

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14. PROGRAMME OUTCOMES IN AI
Program Outcomes (POs) in an AI program serve as the foundation for developing the
critical skills and knowledge necessary for students or graduates to effectively work in
the field of Artificial Intelligence. These outcomes are designed to ensure that graduates
can not only understand theoretical concepts but also apply them practically to solve real-
world challenges. The development of AI is multifaceted, requiring a diverse skill set that
spans technical expertise, problem-solving capabilities, ethical understanding, and the
ability to communicate effectively within multidisciplinary teams. Below is an extended
look into the key program outcomes (POs) for an AI curriculum:
PO1: Problem Solving
The ability to apply AI concepts to solve complex, real-world problems is one of the
primary objectives of an AI program. This outcome emphasizes the importance of
reasoning, optimization, and data analysis. AI professionals must be capable of
formulating and breaking down problems into manageable parts, identifying the best
methods to approach them, and utilizing AI techniques to find effective solutions. This
includes the ability to work with various types of data, such as structured, unstructured,
and temporal data, and apply appropriate machine learning, deep learning, and
optimization algorithms to extract meaningful insights. The goal is to develop problem-
solving skills that enable the student to handle challenges across domains like healthcare,
finance, or robotics.

PO2: Ethical Practices

Ethics in AI is an increasingly important aspect of its development and deployment. AI
professionals must not only be aware of the technical challenges involved but also the
ethical implications of the systems they create. This outcome focuses on ensuring
students understand and can apply ethical principles in AI development. This includes
recognizing potential biases in data or algorithms, ensuring fairness in AI-driven
decision-making, and protecting privacy through responsible data handling practices. As
AI systems are deployed in more sensitive and impactful areas such as healthcare,
criminal justice, and hiring, understanding and mitigating risks related to algorithmic
bias, transparency, and accountability becomes crucial. Students are also taught how to
engage with ethical frameworks and guidelines to make AI development more socially
responsible.
PO3: Programming and Technical Skills
To effectively contribute to the AI field, students must develop proficiency in relevant
programming languages and technical tools. This program outcome emphasizes
proficiency in programming languages like Python, R, and others commonly used in
AI development. Students must also become adept in utilizing AI-focused libraries and

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frameworks such as TensorFlow, PyTorch, Keras, and Scikit-learn. These tools are
essential for implementing machine learning algorithms, designing deep neural networks,
and conducting data analysis. This outcome ensures that graduates have the necessary
technical foundation to write code that supports AI solutions, work with large datasets,
and develop scalable, efficient, and effective AI applications.
PO4: Research and Development
AI is a rapidly evolving field, and its future depends on continuous innovation. Graduates
must be prepared to contribute to ongoing research and the development of cutting-edge
AI technologies. This outcome focuses on developing the ability to conduct research,
synthesize knowledge, and apply AI techniques in new and innovative ways. It includes
the ability to critically evaluate current research, design and implement original
algorithms, and push the boundaries of existing AI capabilities. Whether it's exploring
new machine learning models, improving natural language processing systems, or
inventing novel applications in robotics, students must be equipped with the research
skills necessary to drive progress in AI.
PO5: Communication and Teamwork
AI development is rarely done in isolation. To be successful in AI careers, graduates
must be able to communicate effectively and work collaboratively with interdisciplinary
teams. This outcome stresses the importance of being able to explain complex AI
concepts clearly to both technical and non-technical stakeholders. It also involves
working in teams with individuals from diverse backgrounds, including engineers,
designers, business analysts, and subject matter experts. Whether collaborating on large
AI projects or presenting findings to non-technical audiences, strong communication and
teamwork skills are essential for delivering impactful AI solutions. Additionally, AI
professionals must be able to understand the needs of the end users and design solutions
that are both technically sound and user-friendly.

PO6: Lifelong Learning

The field of AI is evolving at a rapid pace, with new algorithms, frameworks, and
applications emerging continually. As such, a key outcome for AI students is the
commitment to lifelong learning. Graduates must develop the skills to stay updated with
advancements in the field, whether through formal education, attending workshops and
conferences, or self-directed learning. This outcome ensures that AI professionals are
prepared for the changing landscape of the industry, able to learn and adapt to new tools,
technologies, and methodologies. It is crucial for AI practitioners to engage with the
latest research, enhance their skill sets, and adapt to new challenges as the field of AI
matures.

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INTERNSHIP CERTIFICATE:

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