Programme Name : Artificial Intelligence and Machine Learning (AIML)

Modul

e ID

Module Title

Credit

Description

Content

EE950

Data Analytics &

Data Structures

(DADS)

This course is a hands-on introduction to

basic concepts in data analytics, data

structures and visualization. The course

provides the students with a comprehensive

introduction to programming using Python

and shell scripting, enabling them to work in

a linux environment, access remote servers

and effectively debug their code.

Additionally, the course aims to extensively

cover data structures, including their

implementation, manipulation, and analysis,

while also teaching concepts such as file I/O

formats, data readers,data visualization

techniques like t-SNE, and the concept of

Big-O notation. By the end of the course,

students will have gained the necessary

knowledge and tools to analyze data

effectively using Python and navigate the

Linux environment.

● Introduction and Preliminaries

○ Introduction to programming in python

○ Shell scripting

○ Working in Linux

○ Accessing remote servers

○ Debugging

● Data reading

○ File I/O formats

○ Data readers

● Visualization

○ Data Visualization,

○ t-SNE

● Data structures

○ Big-O notation

○ Data structures

EE951

Introduction to

Linear Algebra

This is an introductory linear algebra course

that aims to provide students with a solid

foundation in mathematical concepts and

techniques relevant to machine learning.

This course covers basic linear algebra

topics such as vectors and matrices, singular

value and other decompositions, solving

systems of equations, linear independence,

eigenvalue decomposition, and positive

definite matrices.

● Vectors, vector operations, vector spaces,

matrices, basic matrix operations, matrix

multiplication

● Inner products, norms, linear functions

● Linear systems, LU and QR factorization

● Singular Value Decomposition, Spaces

associated with a matrix,

● Linear independence, Basis and Dimension,

● Solving Ax=b, Determinant,

● Eigenvalues, Eigenvalue decomposition,

Positive Definite Matrices

● Matrix calculus

EE952

Introduction to

Machine Learning

This course aims at introducing the students

to Machine Learning techniques used for

various engineering applications. The

lectures will focus on mathematical principles

and there will be coding based assingments

for implementation, introducing students to

tools such as sklearn and keras.

● Introduction to Preliminaries

○ Classification,Regression,Reinforcement

Learning

○ Evaluation Measures

○ Basic Probability Theory

● Linear Model

○ Linear Regression

○ Linear Classification

● Unsupervised Learning

○ Clustering

○ Gaussian Mixture Model

○ And visualization

● Supervised Learning

○ Regression

○ Image Classification

● Time series Processing

○ Time series Analysis

○ Dynamic Time warping

● ML at Scale

○ Parameter Tuning

○ Model selection

○ Validation and testing

EE953

Basics of

Optimization

This is an introductory optimization course

that seeks to introduce the various

unconstrained optimization methods widely

used in machine learning, particularly

training of supervised models.

● Introduction and Preliminaries

○ Motivation

○ Simple examples

○ Local vs. global optimum

○ Gradient of a function

○ Numerical gradient

● Convexity

○ Convex Sets & functions

○ Convex optimization problems

○ Optimality Condition

● Gradient Descent

○ Narrative Optimization

○ Gradient descent

○ Line search

○ Momentum

● Constrained Optimization

○ Constrained optimization

○ Penalty methods

● Stochastic Gradient Descent

○ Stochastic gradient descent

○ Implementation aspects

EE954

Deep Learning

Fundamentals

The objective of the course is to provide

students with a solid foundation in the

principles, algorithms and techniques of

deep learning. The course aims to enable

students to understand and apply deep

learning models, architectures, and training

methodologies to solve complex problems in

various domains such as computer vision,

natural language processing, and data

analytics.

● Introduction to Deep

○ Overview of neural networks and deep

Learning learning

○ Historical development and key

milestones

○ Applications of deep learning in various

domains

● Artificial Neural Networks

○ Perceptron and multilayer perceptron

○ Activation functions and feedforward

propagation

○ Backpropagation algorithm and gradient

descent

● Optimization Algorithms

○ Stochastic gradient descent (SGD)

○ Adaptive optimization methods (e.g.,

Adam, RMSprop)

○ Regularization techniques (e.g., dropout,

Ll/L2 regularization)

● Convolutional Neural Networks CNNs

○ Motivation and architecture of

○ Convolutional layers and pooling

(CNNs) operations

○ Training CNNs for image classification

● Recurrent Neural Networks (RNNs)

processing

○ Introduction to sequential data

processing

○ Architecture of RNNs and recurrent

cells

○ Training RNNs for sequence modeling

tasks

○ Long Short-Term Memory (LSTM)

Networks

● Generative Models

○ Introduction to generative modeling

○ Variational Autoencoders (VAEs)

○ Generative Adversarial Networks

(GANs)

● Transfer Learning and Pretrained Models

○ Introduction to transfer learning

○ Using pre-trained models for new tasks

EE955

Probability and

Statistics for

Machine Learning

This course aims to provide fundamentals of

probability theory and statistics required for

machine learning. It’s Designed for machine

learning students and aims to provide them

with a solid mathematical foundation in

probability theory and statistics essential for

ML applications . The course covers the

random variables, their

distributions,statistics, and estimation.

● Introduction

○ Introduction to Probability

○ Random Variables

● Distributions

○ Distribution (CDF)

○ Probability Mass Function and

Probability Density Function

○ Conditional Probability and

Independence, The Law of Total

Probability

○ Variance and the Expected Value,

Covariance and Correlation

● Examples of Probability Distributions

○ Discrete RVs e.g. Bernoulli,

Binomial,Poisson Distribution

○ Continuous RVs, e.g. Normal, uniform,

Gamma.

○ Multivariate Distribution

● Statistics

○ Limit theorems, The Law of Large

Numbers The Central Limit

Theorem,Deviation

○ Descriptive Deviation

○ Bayesian Inference

○ Estimation of RVs, Maximum Likelihood

Estimation, Maximum Likelihood

Estimation for Gaussian Distributions

○ Confidence Intervals

○ Hypothesis Testing and P-Values

○ Chi-Square Test for Independence and

Goodness of Fit

EE956

ML for Audio

Processing

This course aims at introducing the students

to machine learning (ML) techniques used

for various audio processing applications.

There will be spectral processing techniques

for analysis and transformation of audio

signals. The lectures will focus on

mathematical principles, and there will be

coding based assignments for

implementation.

● Introduction to speech and music

○ Speech and languages

○ Music: Indian and western

● Digital signal processing

○ Digital signal processing basics

○ Fourier Transforms

○ Pitch and melody

● Machine Learning Review

○ Machine Learning basics

○ Neural Networks (Dense, CNN,

RNN,LSTMs)

● Audio Classification

○ Audio embeddings

○ Radio Scene Classification

● Automatic Speech Recognition (ASR)

○ Acoustic and Language models

○ GMM-HMM based ASR

○ DNN-HMM based ASR

○ End-to-end deep ASR

● Music Information Retrieval

○ Music transcription

○ Music tagging

● Audio Search

○ Embeddings and Hashing

○ Search methods

EE957

Computer Vision

This course provides an introduction to the

field of computer vision, focusing on the

fundamental concepts, algorithms and

applications. Students will learn about image

processing, feature extraction, object

detection, recognition and tracking. The

course will also cover deep learning

techniques for computer vision tasks.

Through lectures, programming assignments

students will gain hands-on experience in

developing computer vision applications.

● Introduction

○ Overview of computer vision

○ Applications of computer vision

○ Human visual system

○ Basics of image formation

● Camera

○ Camera and Photography

○ Camera control concepts

○ Digital image creation: Quantization &

○ Sampling

● Image manipulation

○ Spatial domain: Image enhancement

○ Frequency domain: Image enhancement

● Edge detection

○ Canny edge

○ Hough transform

● Segmentation

○ Thresholding

○ Clustering based segmentation

● Image Compression

○ JPEG compression

○ JPEG 2000

● Image feature representation

○ Feature detector

○ Feature representation

○ Image features: LBP, Haar

● Convolutional neural networks

○ CNN and deep learning

○ Feature representation learning

○ CNN for object detection and recognition

○ Applications and introduction to modern

computer vision

EE958

Natural Language

Processing

Natural language(NL) refers to the language

spoken/written by humans. NL is the primary

mode of communication for humans. With

the growth of the world wide web, data in the

form of text has grown exponentially. It calls

for the development of algorithms and

techniques for processing natural language

for the automation and development of

intelligent machines. This course will

primarily focus on understanding and

developing techniques, statistical learning

algorithms and models for processing

language. We will have a statistical approach

towards natural language processing,

wherein we will learn how one could develop

natural language understanding models from

statistical regularities in large ·corpora of

natural language texts while leveraging

linguistics theories.

● Introduction

○ Introduction

○ Why is NLP hard?

○ Linguistics fundamentals

● Language Models, tagging, and parsing

○ Language Models: n-grams, smoothing,

class-based, brown clustering

○ Sequence Labeling: HMM,

MaxEnt,CRFs, related applications of

these models e.g. Part of Speech

tagging, etc.

○ Parsing: CFG, Lexicalized CFG,

PCFGs,Dependency parsing

● Applications

○ Named Entity Recognition,

○ Coreference Resolution,

○ text classification,

○ toolkits e.g. Spacy etc

● Advanced Topics

○ Distributional Semantics: distributional

hypothesis,vector space model etc.

○ Distributed Representations: Neural

Networks (NN),

Backpropagation,Softmax, Hierarchical

Softmax

○ Word Vectors: Feedforward

NN,Word2Vec, GloVE,

Contextualization(ELMo, etc.), Subword

information

(FastText etc.)

○ Deep Models: RNNs, LSTMs,

Attention,CNNs, applications in

language etc.

○ Sequence to Sequence models:machine

translation and other applications

○ Transformers: BERT, transfer

learning,and applications

○ Graph Neural Networks: basic

architecture, GCN, and applications

EE959

ML with Large

Datasets

This is an introductory optimization course

that seeks to introduce the various

unconstrained optimization methods widely

used in machine learning, particularly

training of supervised models

● Introduction and spark Preliminaries

○ Distributed Computing, databricks

○ Visualization, dimensionality reduction

○ Distributed linear regression

● Basic algorithms

○ Kernel approximations

○ Logistic regression, hashing

○ Distributed trees

● Deep Learning

○ Deep learning, automatic differentiation

○ Large Scale Optimization

○ Optimization for DL

○ Hyperparameter tuning

● Distributed learning

○ Parallel distributed DL

○ Federated Learning

● Advanced Topics

○ Neural architecture search

○ Model compression

EE960

AI in IoT

The objective of the course is to equip

students with the knowledge and skills to

effectively apply artificial intelligence

techniques in the context of Internet of

Things (loT)systems. By covering the basics

of loT communication and security aspects

alongside Al applications, the course aims to

enable students to design, develop, and

deploy intelligent loT solutions that leverage

the power of Al algorithms and models.

● Introduction to loT

○ New Trends and applications

○ loT architecture

○ Middleware

○ Fog computing

○ Sensors and actuators

● loT Communications and Sensor Networks

○ NFC, RFID

○ Bluetooth, Zigbee, Wifi

○ MQTT, HTTP

○ Network Topologies

○ Challenges, Routing and optimization

● loT Security

○ Device security

○ Communication Security

○ Digital Forensics

● Al in loT

○ Smart Cities

○ Healthcare

○ Agriculture

○ Manufacturing

EE961

AI in Healthcare

This course explores the applications of

artificial intelligence (Al) in the healthcare

domain. Students will learn about the

fundamental concepts of Al, machine

learning and deep learning and how they are

applied to various healthcare tasks. The

course will cover topics such as medical

image analysis, clinical decision systems,

electronic health records and personalized

medicine.

● Introduction

○ Overview of Al and its impact on

healthcare

○ Ethical considerations in Al-driven

healthcare

○ Challenges and opportunities in Al

adoption

○ Machine learning fundamentals

● Visual data analysis in medical domain

○ Feature engineering and feature

selection for medical domain

○ Introduction to medical imaging

modalities (e.g., X-ray, MRI)

○ Image data formats in medical domain

○ Image segmentation and feature

extraction

○ Deep learning for medical image

analysis

○ Instruments and sensor analysis in

medical domain

● ML and rule based disease diagnosis

○ Role of Al in clinical decision making

○ Rule-based systems and expert systems

○ Machine learning models for diagnosis

and prognosis

○ Explainability and interpretability in

clinical decision support

● Healthcare records processing and robotics

in healthcare

○ Overview of electronic health records

○ Data mining techniques for EHR

analysis

○ Predictive modeling using EHR data

○ Al approaches in drug discovery and

development

○ Virtual screening and molecular docking

○ Genomic data analysis and personalized

medicine

○ Robotics applications in surgery and

Rehabilitation

○ Surgical planning and assistance

systems

○ Human-robot interaction in healthcare

settings

EE962

Industrial AI and

Automation/ AI in

Industry and

Automation

This course explores the applications of

artificial intelligence (Al) in industrial settings

and automation processes. Students will

learn about the use of Al techniques such as

machine learning, robotics, sensory data and

image processing in various industries. The

course will cover topics such as smart

manufacturing, predictive maintenance,

supply chain optimization and intelligent

automation. Through lectures, case studies

and hands-on assignments students will gain

an understanding of how Al is transforming

industries and enabling efficient and

intelligent automation.

● Introduction to Al in Industry and

Automation

○ Overview of Al and its impact on

industries

○ Role of automation in industrial

processes

○ Challenges and opportunities in

adopting AI in industry

● Robotics and automation

○ Introduction to Industrial Robotics

○ Robot kinematics and dynamics

○ Robot control systems and

Programming

○ Collaborative robots and human-robot

interaction

● Industry 4.0

○ Concepts of smart manufacturing and

Industry 4.0

○ Al-enabled quality control and defect

detection

○ Predictive maintenance and condition

monitoring

○ Digital twin technology and virtual

Commissioning

● Future trends and emerging applications

○ Optimization of supply chain processes

○ Demand forecasting using Al techniques

○ Route optimization and fleet

Management

○ Warehouse automation and inventory

management

○ Cognitive automation and decision

support systems

○ Workflow automation and business

process optimization

○ Fraud detection and risk assessment

using Al

○ Algorithmic trading and portfolio

Management

○ Chatbots and virtual assistants in

Banking

○ Al in agriculture and food production

○ Al in transportation and autonomous

vehicles

EE963

Reinforcement

Learning

In this course we will explore how an agent

(via interactions with the environment) can

learn by trial and error. This is quite different

from supervised machine learning and

comes close to how humans learn by

interactions. Reinforcement Learning (RL)

● Introduction

○ RL task formulation

○ Action space, state space, environment

● Dynamic Programming

○ Tabular based solution

○ Dynamic Programming

deals with problems that require sequential

decision making. This course will explore

foundations of reinforcement learning. We

will study different algorithms for RL and later

in the course we will explore how functional

approximation in RL algorithms could be

done using neural networks giving rise to

deep reinforcement learning.

○ Monte Carlo

○ Temporal Difference

● Functional Approximation and Deep RL

○ Value based Deep Reinforcement

Learning : Functional Approximation in

RL, NFQ (Neural Fitted Q Iteration),

DQN (Deep Q- Network), Double

DQN,Dueling DDQN, PER (Prioritized

Experience Replay)

○ Policy Based and Value Based

Algorithms: REINFORCE, Vanila Policy

Gradient (VPG), A3C {Asynchronous

Advantage Actor Critic), Generalized

Advantage Estimation {GAE),Advantage

Actor-Critic {A2C), SARSA

○ Advanced Actor Critic: DDPG {Deep

Deterministic Policy Gradient),

TD3(Time Delayed DDPG), SAC {Soft

Actor Critic), PPO (Proximal Policy

Optimization)

● Advanced Topics

○ Model-based RL

○ Imitation Learning

○ Meta-Learning

○ Multi-agent Learning

○ POMDP

EE964

Project

The goal of this module is to have the

students do an industry-relevant project

in a topic related to machine learning. A

module may have one or more

instructors, who will decide the topics of

the projects in consultation with the

enrolled instructors. The project will

typically involve design and development

● Sentiment analysis

● Advanced image classification

● Fraud Detection

● Recommendation Systems

● Spam Email Classification

● Disease Diagnosis

● Stock Price Prediction

● Object Detection

● Facial Emotion Recognition

of a novel ML model or algorithm. A

specific project may be split across at

most two modules.

● Natural Language Generation

EE965

Unsupervised

Learning

The objective of this course is to

introduce the students to unsupervised

machine learning techniques used for

various engineering applications. The

students will gain the skills to extract

valuable insights from datasets lacking a

specified target or labeled variable. The

lectures will focus on underlying

mathematics principles as well as

application problems in various domains.

The Students will also be introduced to

unsupervised learning libraries such as

sklearn.

● Introduction and K-means Clustering

● Hierarchical and Spectral Clustering

● Dimension Reduction- Linear and Nonlinear

● Matrix Factorization, NMF optimization

● Graphical Models, Bayesian Networks, Markov

Random Fields

● Mixture Models and EM

● Approximate Inference

EE966

AIML Projects with

real-world

datasets

As part of the course,students will

participate and successfully complete

several PYTHON-based projects and

case studies on key AI/ML techniques

such as linear Regression,Logistic Re

● Introduction to PYTHON, ML Packages,

Data compression, Principal Component

Analysis (PCA)

○ Introduction to PYTHON libraries

SCIKIT, PANDAS, NUMPY,

Introduction to Introduction to PCA

algorithm, PCA via SVD.

○ Project 0: SCIKIT, PANDAS, NUMPY

and other ML modules in PYTHON

○ Project 1: PCA-Based clustering for

IRIS dataset

● Linear Regression

○ Regression applications, Problem

formulation and solution

○ Project 2: IRIS Dataset Regression using

PYTHON

○ Project 3: Boston Housing Price

Analysing using PYTHON-Based

Regression

○ Project 4: California Housing Price

Analysing using PYTHON-Based

Regression

● Logistic Regression

○ Logistic function, Likelihood

maximization, Online learning for

parameter estimation

○ Project 5: SCIKIT Package for Logistic

Regression using Purchase/ Shopping

Data

○ Project 6: Logistic regression application

for Wine quality dataset

● Support Vector Machines

○ SVM applications, Maximum margin

classifier, Kernel SVM

○ Project 7: Breast Cancer Dataset

Analysis using SVC

○ Project 8: IRIS Data Set classification

using PYTHON-Based SVC

● Naïve Bayes

○ Discrete feature vectors, Naïve Bayes

assumption, Calculation of posterior

probabilities, Laplacian smoothing

○ Project 9: Naïve Bayes Clustering of

Purchase Dataset using SCIKIT library

○ Project 10: Wine quality data set

classification using Naïve Bayes

● Linear Discriminant Analysis

○ Multivariate Gaussian modeling,

Likelihood Ratio test, Discriminant

function

○ Project 11: Discriminant Based Data

Classification using IRIS Data Set

● Decision Tree Classifiers (DTC)

○ DTC structure, choice of best attribute,

Concept of Entropy, Mutual Information

or Information Gain

○ Project 12: Building a Decision Tree

Classifier using the Purchase Logistic

Data Set

○ Project 13: Building a Decision Tree

Classifier for IRIS Dataset using

PYTHON

● K-Means and Probabilistic Clustering

○ Unsupervised learning, K-Means

procedure, EM Algorithm, Soft

clustering

○ Project 14: Clustering Analysis using

PYTHON

EE967

Deep Learning

and Neural

Networks (DLNN)

Projects with real-

world datasets

As part of the course, students will

participate and successfully complete

several PYTHON (TensorFlow and

Keras)-based projects and case-studies on

key Deep Learning (DL)/ Neural Network

(NN) techniques such as Neurons,

Activation Functions, Deep Neural

Networks and Convolutional Networks

in significant detail. These projects will be

based on practical real-world datasets

such as Boston Housing Price, California

Housing Price, Mobile Phone Dataset,

 Introduction to PYTHON, DL Packages and

Basics of Neural Networks: Introduction to

PYTHON libraries, Tensorflow, Keras,

Introduction to Neural Networks, Structure of

Neural Nets, Properties of Neurons and

Activation



Projects on Neural Networks for Boston and

California Housing Price Datasets: Projects on

Neural Network for the Housing Datasets

 Project 1: Boston Housing Price Analysing

using Neural Network



Project 2: California Housing Price Analysis

Fashion Dataset, Handwritten Digit

Classification Dataset, IMDB movie rating

and others. Another important aspect of

the program is to study Structure of

Neural Units, Properties of Neurons,

Back Propagation, Convolution, Pooling

and Flattening operations etc. Students

will also develop the skills to effectively

use integrated development environments

(IDEs) in PYTHON and advanced packages

such as TensorFlow and Keras for

tackling more extensive DL/ NN projects

in the future

using PYTHON-Based Neural Networks

 Project on for analysis of Mobile Phone

Prices: Project on Neural Networks for analysis

of Mobile Prices Dataset. Use various

parameters such as clock speed,dual sim,

carrier, 4G, memory, cores, pixel height, pixel

width etc to predict price range.

 Deep Learning, Multi-layer Neural Networks:

Architecture of Deep Neural Networks,

Mathematical Analysis of Back Propagation,

Algorithm for Back Propagation with Arbitrary

number of layers.



Deep Learning Project for Fashion

Classification using the MNIST Fashion Data:

Project on Deep Learning Project for Fashion

Data. Project is built using the MNIST Fashion

Dataset. This is a dataset with fashion article

images—consisting of a training set of 60,000

examples and a test set of 10,000 examples.

Each example is a 28x28 grayscale image,

associated with a label from 10 classes.



Deep Learning Project for Digit

Classification : Project on Deep Learning for

Digit Classification using Digit Data Set. This

project uses the MNIST ("Modified National

Institute of Standards and Technology") dataset

which is the de facto dataset of computer vision.

This classic dataset of handwritten images has

served as the basis for benchmarking

classification algorithms. The goal of this project

is to use deep learning to correctly identify

digits from a dataset of tens of thousands of

handwritten images

 Convolutional Neural Networks (CNNs):

Introduction to CNNs, Basic structure of CNNs,

Activation Maps in CNNs, Pooling and flattening

operations in CNNs.



Projects on Deep Learning using the CIFAR

Dataset, Deep Learning for Movie Rating

Classification using IMDB Dataset: Projects on

Deep Learning using the CIFAR Dataset. The

CIFAR-10 dataset consists of 60000 32x32

colour images in 10 classes, with 6000 images

per class. There are 50000 training images and

10000 test images. IMDB Dataset and Deep

Learning for Movie Rating Classification