PG Diploma in AIML

Course ID Course Title Credit Description Content
IS901E Introduction to Python and Agentic AI 9 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. The course will also include AI agents used for automating AI-driven NLP tasks.
  • Introduction and Preliminaries
  • Data Reading
  • Visualization
  • Data structures
  • Prompting and AI Agents
IS902E Linear Algebra 9 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.
  • Vectors and Matrices
  • Inner Products and Norms
  • Linear Systems
  • Singular Value Decomposition
  • Linear Independence, Basis, Dimension
  • Solving Ax=b
  • Eigenvalues, PD Matrices
  • Matrix Calculus
IS903E Introduction to ML 9 This course aims to introduce the students to machine learning (ML) techniques used for various engineering applications. The lectures will focus on mathematical principles, and there will be coding-based assignments for implementation, introducing students to tools such as sklearn and keras.
  • Preliminaries, basics, applications
  • Model Evaluation Measures
  • Supervised Learning
  • Unsupervised Learning
  • Neural Network basics
  • ML at scale, MLops
IS904E Probability and Statistics for ML 9 This course aims to provide the fundamentals of probability theory and statistics required for machine learning.
  • Introduction to Probability and Random Variables
  • Distributions, expectation, variance, and covariance
  • Discrete and continuous RVs
  • Statistics, limit theorems, inference
  • Hypothesis testing, statistical tests
IS905E Optimization and Deep Learning 9 This course aims to equip students with foundational and advanced techniques in optimization essential for deep learning. It covers classical and modern optimization methods, including gradient-based approaches and stochastic algorithms. Students will also gain hands-on understanding of neural network architectures such as CNNs, RNNs, and Transformers.
  • Introduction, random and grid search
  • Univariate and multivariate optimization
  • Gradients, gradient descent, convexity
  • Newton and quasi-newton
  • Stochastic gradient descent
  • Introduction to Neural Networks, Forward and Backpropagation
  • CNNs, RNN, Transformers
IS906E Human-Computer Interaction 9 This course introduces the principles and practices of Human-Computer Interaction (HCI), focusing on user-centered design and evaluation. It covers methods for need finding, prototyping, and interaction modeling, with emphasis on ethical and inclusive design. Students will explore HCI applications in AI, creativity, collaboration, and learning contexts
  • Introduction and basics of human cognition (perception, memory, attention, input/output modes)
  • Design thinking for HCI & its stages – Need finding and qualitative analysis
  • Prototyping techniques and basics of design (gulf of execution & evaluation, affordances & signifiers, mental models)
  • Fonts, colors, layouts, visualizations
  • Ethics and human-subject studies
  • Prototype evaluation (heuristics, controlled experiments, usability studies)
  • Information design & Human-information interaction
  • UI/UX for mobile devices
  • Human-AI interaction & decision making
  • Human-robot interaction
IS907E AIML Projects with real-world datasets 9 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 Regression, Support Vector Machines, Linear Discriminant Analysis in significant detail. These projects will be based on practical real-world datasets such as IRIS, Boston Housing Price, Breast Cancer parameters, Mobile Phone Prices, California Housing Price, Wine quality and others. Another important aspect of the program is to study applications of data pre-processing techniques such as Principal Component Analysis for feature selection. Projects will also be discussed for user clustering, such as K-means, Probabilistic Clustering, Naïve Bayes and Decision Tree Classifiers. Students will also develop the skills to effectively use integrated development environments (IDEs) in PYTHON for tackling more extensive AI/ ML projects in the future
  • Introduction to PYTHON, ML Packages, Data compression, Principal Component Analysis (PCA) Project 0: SCIKIT, PANDAS, NUMPY and other ML modules in PYTHON Project 1: PCA-Based clustering for IRIS dataset
  • Linear Regression 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 Project 5: SCIKIT Package for Logistic Regression using Purchase/ Shopping Data Project 6: Logistic regression application for Wine quality dataset
  • Support Vector Machines Project 7: Breast Cancer Dataset Analysis using SVC Project 8: IRIS Data Set classification using PYTHON-Based SVC
  • Naïve Bayes Project 9: Naïve Bayes Clustering of Purchase Dataset using SCIKIT library Project 10: Wine quality data set classification using Naïve Bayes
  • Linear Discriminant Analysis Project 11: Discriminant Based Data Classification using IRIS Data Set
  • Decision Tree Classifiers (DTC) 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 Project 14: Clustering Analysis using PYTHON
IS908E Unsupervised Learning 9 This course introduces key concepts and techniques in unsupervised machine learning, including clustering, dimensionality reduction, and probabilistic modeling. Students will explore methods such as K-means, spectral clustering, graphical models, and mixture models with EM. The course also covers advanced topics like approximate inference and deep generative models including VAEs and GANs.
  • Introduction and K-means Clustering
  • Hierarchical and Spectral Clustering
  • Dimension Reduction – Linear and Nonlinear
  • Probabilistic ML
  • Graphical Models, Bayesian Networks, Markov Random Fields
  • Graphical Models, Bayesian Networks, Markov Random Fields
  • Mixture Models and EM
  • Approximate Inference
  • Deep Generative Models (VAE and GAN)
ECO921E Economics and Governance of AI 9 Course Description: This course addresses the economics, governance, and regulation of artificial intelligence. It covers AI’s role in productivity, labor markets, competition, ethics, and public policy.
Objective: -Analyze the economic impact of AI on growth, labor, and inequality.
- Understand governance models for AI at national and global levels.
- Critically evaluate regulatory frameworks, ethics, and geopolitics.
- Examine India’s AI policy and global strategies
  • Economics of AI AI as GPT; productivity, growth, innovation
  • AI & Labor Markets Automation, augmentation, displacement, reskilling
  • AI in Business & Platforms Competitive advantage, data driven strategies
  • Governance Models Self-regulation, national policy frameworks
  • Global AI Geopolitics U.S., EU, China, India; international cooperation
  • Regulation & Ethics Bias, transparency, accountability, safety
  • AI for Public Policy AI in Governance, Education, Healthcare.
  • Case Study and Features Indian AI Initiatives , frontier risks