# Introduction to Artificial Intelligence, GenAI, Agentic AI - Saturday (08.11.2025)
course: Module 1 — Foundations of AI & ML
module: Module-1-Foundations-AI-ML
type: pdf
source_url: https://personal-learn.armco.dev/files/Module-1-Foundations-AI-ML/General/Introduction_to_Artificial_Intelligence,_GenAI,_Agentic_AI_-_Saturday_(08.11.2025).pdf
pages: 36
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[page 1]
IIT Roorkee – Futurense
PG Certification
GenAI and Agentic AI for
Engineers
Dr Durga Toshniwal
Professor (HAG)
Department of Computer Science & Engg.
Indian Institute of Technology Roorkee
durga.toshniwal@cs.iitr.ac.in, durgatoshniwal@gmail.com
www.durgatoshniwal.in
[page 2]
Introduction to
Artificial Intelligence,
GenAI, Agentic AI
GenAI and Agentic AI - Dr Durga Toshniwal
[page 3]
Cognitive Science
GenAI and Agentic AI - Dr Durga Toshniwal
• Process of knowing
[page 4]
Cognitive Science
• Cognitive science is the interdisciplinary, scientific study
of the mind and its processes.
• Cognitive scientists study intelligence and behavior, with
a focus on how nervous systems represent, process, and
transform information.
GenAI and Agentic AI - Dr Durga Toshniwal
[page 5]
What is Artificial Intelligence ?
• Making computers that think?
• The automation of activities we associate with human
thinking, like decision making, learning ... ?
• The art of creating machines that perform functions that
require intelligence when performed by people ?
GenAI and Agentic AI - Dr Durga Toshniwal
[page 6]
What is Artificial Intelligence ?
• A field of study that seeks to explain and emulate
intelligent behaviour in terms of computational
processes ?
• A branch of computer science that is concerned with
the automation of intelligent behaviour ?
GenAI and Agentic AI - Dr Durga Toshniwal
[page 7]
• Artificial
– Produced by human art or effort, rather than
originating naturally.
• Intelligence
– is the ability to acquire knowledge and use it"
• So AI can be defined as:
– AI is the part of computer science concerned
with design of computer systems that exhibit
human intelligence
What is Artificial Intelligence ?
GenAI and Agentic AI - Dr Durga Toshniwal
[page 8]
From the above definition, we can see that
AI has two major roles:
– Study the intelligent part concerned with
humans.
– Represent those actions using computers.
GenAI and Agentic AI - Dr Durga Toshniwal
[page 9]
Artificial Intelligence
GenAI and Agentic AI - Dr Durga Toshniwal
[page 10]
AI Tasks
Artificial intelligence can be considered under a number of
headings:
– Search
– Representing Knowledge and Reasoning with it.
– Planning.
– Learning.
– Natural language processing.
– Expert Systems.
– Interacting with the Environment
(e.g. Vision, Speech recognition, Robotics)
GenAI and Agentic AI - Dr Durga Toshniwal
[page 11]
Learning
• If a system is going to act truly appropriately, then
it must be able to change its actions in the light of
experience:
– how do we generate new facts from old ?
– how do we generate new concepts ?
– how do we learn to distinguish different
situations in new environments ?
– Machine Learning – Making machines
intelligent
GenAI and Agentic AI - Dr Durga
Toshniwal
[page 12]
GenAI and Agentic AI - Dr Durga Toshniwal
Major
AI Techniques
Supervised
Learning
- Classification
and Prediction
Regression
Un-
Supervised
Learning
- Clustering
[page 13]
Supervised Learning
(Classification) based on
Discriminative Models
GenAI and Agentic AI - Dr Durga Toshniwal
[page 14]
Classification: Definition
• Given a collection of records (training set )
– Each record contains a set of attributes, one of the
attributes is called the class label or classifying attribute.
• Find a model for class attribute as a function of the values
of other attributes.
• Is the Task of learning a function that maps an input to an
output
• Goal: previously unseen records should be assigned a
class as accurately as possible.
– A test set is used to determine the accuracy of the
model. Usually, the given data set is divided into training
and test sets, with training set used to build the model
and test set used to validate it.
GenAI and Agentic AI - Dr Durga Toshniwal
[page 15]
Classification: Application 1
• Direct Marketing
– Goal: Reduce cost of mailing by targeting a set of
consumers likely to buy a new product.
– Approach:
• Use the data for a similar product introduced before.
• We know which customers decided to buy and which decided
otherwise. This {buy, don’t buy} decision forms the class
attribute.
• Collect various demographic, lifestyle, and company-interaction
related information about all such customers.
– Type of business, where they stay, how much they earn, etc.
• Use this information as input attributes to learn a classifier
model.
From [Berry & Linoff] Data Mining Techniques, 1997
GenAI and Agentic AI - Dr Durga Toshniwal
[page 16]
Classification: Application 2
• Fraud Detection
– Goal: Predict fraudulent cases in credit card
transactions.
– Approach:
• Use credit card transactions and the information on its
account-holder as attributes.
– When does a customer buy, what does he buy, how often he
pays on time, etc.
• Label past transactions as fraud or fair transactions. This
forms the class attribute.
• Learn a model for the class of the transactions.
• Use this model to detect fraud by observing credit card
transactions on an account.
GenAI and Agentic AI - Dr Durga Toshniwal
[page 17]
Discriminative Models
• Discriminative AI models are a class of ML
models that focus on learning the decision
boundary between different classes of data.
• They aim to distinguish between categories
rather than generate new examples.
• These models learn to map input features directly
to labels i.e., they model the probability of a label
given the data
GenAI and Agentic AI - Dr Durga Toshniwal
[page 18]
Discriminative Models
Key Characteristics of Discriminative Models:
• Goal: Classify or predict labels from input data.
• Function: Focus on finding the boundary that separates
classes.
• Output: Probabilities or class labels (e.g., "spam" or "not
spam").
GenAI and Agentic AI - Dr Durga Toshniwal
[page 19]
Un-Supervised Learning
(Clustering)
GenAI and Agentic AI - Dr Durga Toshniwal
[page 20]
Clustering Definition
• Given a set of data points, each having a
set of attributes, and a similarity measure
among them, find clusters such that
– Data points in one cluster are more similar to
one another.
– Data points in separate clusters are less
similar to one another.
• Similarity Measures:
– Euclidean Distance
– Other Problem-specific Measures.
GenAI and Agentic AI - Dr Durga Toshniwal
[page 21]
Clustering Contd.
Euclidean Distance Based Clustering in 3-D space.
Intracluster distances
are minimized
Intercluster distances
are maximized
GenAI and Agentic AI - Dr Durga
Toshniwal
[page 22]
Histograms on First Names
GenAI and Agentic AI - Dr Durga Toshniwal
[page 23]
Clustering: Application 1
• Market Segmentation:
– Goal: subdivide a market into subsets of
customers where any subset may be selected as
a marketing target.
– Approach:
• Collect different attributes of customers based on
their geographical and lifestyle related information.
• Find clusters of similar customers.
• Measure the clustering quality by observing buying
patterns of customers in same cluster vs. those
from different clusters.
GenAI and Agentic AI - Dr Durga Toshniwal
[page 24]
Clustering: Application 2
• Document Clustering:
– Goal: To find groups of documents that are similar to
each other based on the important terms appearing in
them.
– Approach: To identify frequently occurring terms in
each document. Form a similarity measure based on
the frequencies of different terms. Use it to cluster.
– Gain: Information Retrieval can utilize the clusters to
relate a new document or search term to clustered
documents.
GenAI and Agentic AI - Dr Durga Toshniwal
[page 25]
Clustering: Application 3
Discovered Clusters Industry Group
1
Applied-Matl-DOW N,Bay-Network-Down,3-COM-DOWN,
Cabletron-Sys-DOWN,CISCO-DOWN,HP-DOWN,
DSC-Comm-DOW N,INTEL-DOWN,LSI-Logic-DOWN,
Micron-Tech-DOWN,Texas-Inst-Down,Tellabs-Inc-Down,
Natl-Semiconduct-DOWN,Oracl-DOWN,SGI-DOW N,
Sun-DOW N
Technology1-DOWN
2
Apple-Comp-DOW N,Autodesk-DOWN,DEC-DOWN,
ADV-Micro-Device- DOWN,Andrew-Corp-DOWN,
Computer-Assoc-DOWN,Circuit-City-DOWN,
Compaq-DOWN, EMC-Corp-DOWN, Gen-Inst-DOWN,
Motorola-DOW N,Microsoft-DOWN,Scientific-Atl-DOWN
Technology2-DOWN
3
Fannie-Mae- DOWN,Fed-Home-Loan-DOW N,
MBNA-Corp-DOWN,Morgan-Stanley-DOWN Financial-DOWN
4
Baker-Hughes-UP,Dresser-Inds-UP,Halliburton-HLD-UP,
Louisiana-Land-UP,Phillips-Petro-UP,Unocal-UP,
Schlumberger-UP Oil-UP
• Observe Stock Movements every day.
• Clustering points: Stock-{UP/DOWN}
• Similarity Measure: Two points are more similar if the events
described by them frequently happen together on the same day.
GenAI and Agentic AI - Dr Durga
Toshniwal
[page 26]
Regression and Forecasting
GenAI and Agentic AI - Dr Durga Toshniwal
[page 27]
Predictive Analytics – Understanding
the Future (Forecasting)
• Regression can be used for forecasting values and
also for prediction
• Predictive analytics analyzes current and historical facts
to make predictions about future, or otherwise unknown,
events.
• One of the most well known applications is credit
scoring, for financial services. Scoring models process a
customer's credit history, loan application, customer data,
etc., in order to rank-order individuals by their likelihood of
making future credit payments on time.
GenAI and Agentic AI - Dr Durga Toshniwal
[page 28]
Anomaly Detection
• Detect significant deviations from normal behavior
• Supervised / Un-Supervised Methods can help
• Applications:
– Credit Card Fraud Detection
GenAI and Agentic AI - Dr Durga Toshniwal
[page 29]
Deep Learning
GenAI and Agentic AI - Dr Durga Toshniwal
• Deep Learning is a subset of ML that uses artificial
neural networks with many layers ("deep“) to model and
learn complex patterns in data.
• These neural networks are inspired by the human brain's
architecture and are capable of automatically learning
feature representations from raw input, such as images,
text, or audio.
• Unlike traditional ML, which often requires manual feature
extraction, deep learning models like Convolutional
Neural Networks (CNNs) can learn patterns directly from
large datasets through multiple layers of nonlinear
processing.
[page 30]
Applications of Deep Learning
GenAI and Agentic AI - Dr Durga Toshniwal
• Computer Vision: Used in facial recognition, medical
imaging diagnostics, autonomous vehicles (object
detection), etc.
• Natural Language Processing (NLP): Helps in language
translation, chatbots, sentiment analysis, and large
language models like ChatGPT.
• Speech Recognition: Enables voice assistants like Siri,
Alexa, and real-time transcription tools.
• Healthcare: Assists in disease detection, personalized
treatment recommendations, and drug discovery.
• Finance: Applied in fraud detection, algorithmic trading,
and credit risk modeling.
[page 31]
Some AI Application Domains
• Computer Vision
• Social Media Data Analytics
• Natural Language Processing .
• Generative AI
• Agentic AI
GenAI and Agentic AI - Dr Durga Toshniwal
[page 32]
Generative AI (GenAI)
GenAI and Agentic AI - Dr Durga Toshniwal
• Generative AI refers to a class of AI models designed to
create new content such as text, images, code, or even
synthetic data that mimics real-world examples.
• Are capable of understanding context, style, and structure
in a way that allows them to produce human-like content.
• The traditional AI systems are primarily discriminative i.e., they
classify or predict
• Generative AI learns patterns from vast amounts of data
and uses those patterns to generate novel outputs.
• These models, are often based on deep learning architectures
like Generative Adversarial Networks (GANs), Variational
Autoencoders (VAEs), and large language models (LLMs) such
as GPT and BERT,
[page 33]
Generative AI (GenAI)
GenAI and Agentic AI - Dr Durga Toshniwal
• Generative AI refers to a class of AI models designed to
create new content such as text, images, code, or even
synthetic data that mimics real-world examples.
• Are capable of understanding context, style, and structure
in a way that allows them to produce human-like content.
• The traditional AI systems are primarily discriminative i.e., they
classify or predict
• Generative AI learns patterns from vast amounts of data and
uses those patterns to generate novel outputs.
• These models, are often based on deep learning architectures
like Generative Adversarial Networks (GANs), Variational
Autoencoders (VAEs), and large language models (LLMs) such
as GPT and BERT,
[page 34]
Generative AI (GenAI)
GenAI and Agentic AI - Dr Durga Toshniwal
Feature Discriminative
Model Generative Model
Learns ( P(y x) )
Purpose Classify or predict labels Generate new samples
Example Task Spam detection Text generation
Model Example Logistic Regression GPT, BERT (pretraining),
GANs etc.
[page 35]
Agentic AI
GenAI and Agentic AI - Dr Durga Toshniwal
• Agentic AI refers to AI systems that behave like
autonomous agents that are capable of making
decisions, setting goals, planning, and acting in
dynamic environments without constant human
intervention.
• These systems go beyond simple task execution and
exhibit goal-directed behavior, often with the ability to
adapt their strategies, reason about consequences,
and interact with other agents or humans.
• Agentic AI combines elements of reinforcement learning,
planning, natural language, and reasoning to simulate a
form of “agency” similar to how humans operate in
complex tasks.
[page 36]
Applications of Agentic AI
GenAI and Agentic AI - Dr Durga Toshniwal
• Personal AI Assistants: Systems like advanced virtual
agents that can autonomously manage calendars, send
emails, plan trips, or do bookings based on evolving user
preferences.
• Autonomous Robotics: Robots in logistics, manufacturing,
or healthcare that navigate spaces, make task decisions,
and adapt to changes in real-time.
• AI in Software Development: Tools that can autonomously
write and debug code based on high-level objectives.
• Scientific Discovery: Agentic AI systems can
autonomously design and run experiments, interpret data,
and refine hypotheses e.g., in drug discovery or materials
science.