Online PG Diploma in AI-ML for Managers (PGDAIMLM Batch 1)

Applications of Decision Science (DS) and Operations Research (OR) in business

Use of OR in operations, supply chain, marketing, finance, and HR

Case-based learning approach for real-world problem solving

Data-driven decision-making techniques

Optimization methods for improved business outcomes

Principles of human-computer interaction in data visualization

Visual encoding for effective data communication

Handling information overload using visual design

Techniques: heat maps, infographics, and dashboards

Multidimensional data visualization and graphical perception

Mapping, cartography, and text visualization

Visualization for improved comprehension and decision-making

Internet as a research and data collection medium

Research design and sampling techniques

Online surveys and non-reactive data collection

Virtual ethnography and online focus groups

Blogs, videos, and secondary qualitative data sources

Data analysis approaches and research tools

Use of Generative AI for research and content creation

Prompt engineering for non-technical users.

Fundamentals of machine intelligence and learning

Linear learning models

Artificial Neural Networks (single-layer and multi-layer)

Backpropagation and learning variants

Support Vector Machines (classification and regression)

Learning theory and model complexity (VC dimension, PAC learning)

Unsupervised learning: PCA and KPCA

Clustering techniques

Feature selection methods

Introduction to semi-supervised learning

Fundamentals of Database Management Systems (DBMS)

Hierarchical and multidimensional data modeling

Data warehouse design and ETL processes

SQL for data warehousing

OLAP and OLTP concepts

Data warehousing risks and management issues

Designing and expanding data warehouse applications.

Nonlinear dimensionality reduction techniques

Maximum entropy and exponential family models

Graphical models

Computational learning theory

Structured Support Vector Machines

Feature and kernel selection methods

Meta-learning and multi-task learning

Semi-supervised and reinforcement learning

Approximate inference methods

Clustering and boosting techniques

Overview of enterprise-level AI/ML systems

AI/ML use cases across enterprises

Managerial and operational challenges of AI/ML systems

Economic assessment of AI/ML projects

Effort estimation, pricing, and costing models

Responsible AI: fairness, ethics, transparency, accountability

Governance frameworks for AI/ML systems

Risks, unintended consequences, and policy interventions

Emerging research and practice in IT management

Contemporary and evolving technology topics

Industry-relevant and research-driven themes.

Emerging topics in information processing

Advanced concepts and applications

Research-oriented and practice-focused modules

Advanced and specialized topics in Machine Learning

Deep Learning concepts and applications

Current research and development challenges

Emerging trends in ML and AI

Capstone Project in Artificial Intelligence and Machine Learning - Hands-on project undertaken in groups of 4 members where students learn to solve complex problems by using advanced machine learning algorithms on large complex datasets.

Online PG Diplomas are academic programmes of IIT Delhi, and there is no campus placement or placement assistance provided by IIT Delhi for these programmes.

Evaluation of minor projects is subject to faculty discretion, based on academic guidelines and instructional objectives.

Assessment criteria may vary depending on the nature of the project and its alignment with the course framework.

Modules/topics are indicative only, and the suggested time and sequence may be dropped/ modified/ adapted to fit the total programme hours. Case studies, real-world examples, and numerical illustrations are an integral part of multiple modules included in the course.

The primary mode of learning for this programme is via live online sessions with faculty members. Post-session video recordings will be made available for the duration of the programme.

Emeritus or the institute does not guarantee the availability of any session recordings.

Fundamentals of Python will be taught via recorded sessions. The faculty will be conducting Q/A on the same.

The sessions will be delivered by IIT Delhi faculty and industry experts, brought by the Programme Coordinator only.

Curriculum is subject to change and modification as per the requirements of the programme.

IIT Delhi and the Programme Coordinator's decision will be final.