Programme Content
Introduction to AI-Ready Data & Modern Data Ecosystems, Sampling and How AI interprets Data
Data Visualisation - Methods and Approaches in Computer Human Interaction Principles (Tableau)
Responsible AI Systems - Design principles, Fairness, Accountability, Transparency, Ethics, UX & Regulations
Multidimensional Data handling, Regression, Model Explainability, Feature Selection, Unsupervised Machine Learning
Advanced Supervised and Unsupervised Machine Learning for Classification, Association Rule Mining, Outlier Detection, and Sequence Mining
Data Model Building for ML and Big Data Feature Engineering applications - Boston Case Study
Machine Learning using Artificial Neural Networks (Concepts of Apriori, Back Propagation, Feedback, Loss Functions)
Supervised ML - Decision Trees, Random Forest, SVM, Naïve Bayes Classifiers, Ensemble Learning, XG Boost
Generative AI and Chatbots: Large Language Models using RNN, LSTM and Transformers (Chain-of-thought, Planning, Reflection)
Deep Learning for Computer Vision Using Convoluted Neural Networks, Gradient functions
NLP in Social Media Analytics - Sentiment Analysis, Text Summarisation, Emotion Analysis, Topic Modelling, LDA, LSA
Network Science for Large Graphs with Graph Theory, Hands-on Exercises with Small Networks Data
No Code Supervised AI - Gradient Boosting, Ensemble Learning, ANN, SVM, RF, DT, NBC
No Code Unsupervised AI - Clustering, NLP, Topic Modeling, Sentiment mining
Network Science, Graph Assisted Rankings and GenAI in Search Ecosystems: The Google Case and BERT
Agentic AI models, Planning, Execution, RAG Workflows
Data Science Capstone Project - Machine Learning Implementations involving NLP/LLM/Large Datasets
Individual Evaluation on Artificial Intelligence and Machine Learning
Understanding Main Pillars of Business Decision Science and Heuristics/Meta-Heuristics/AI
Central Limit Theorem, Distributions, Dispersion, Population, Sample T Test, Z Test, Chi Square Test
Comparing Multiple Groups - ANOVA, MANOVA
Introduction to Linear Programming (Single Objective) and solving using Solver/ LINGO
Sensitivity Analysis using Solver/LINGO
Goal Programming (Multiple Objectives) Using Solver/LINGO
Application of LP/NLP in Business Decisions Through Case Study
Genetic and Memetic Algorithms
Time Series Analysis (Moving Average, Exponential)
Time Series Analysis (Holtz and Winter-Holts Model)
Auto Regressive Integrated Moving Average Models
Multi Criteria Decision Making: ISM, Hands on ISM
Multi Criteria Decision Making: DEMATEL, AHP
Multi Criteria Decision Making: TOPSIS
Descriptive, Predictive and Prescriptive Decision Science
11. A case-study-based project where participants must provide business solutions using Python, Excel, or LINGODisclaimer:
This programme is an advanced certification from 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.
Note:
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 by live online sessions with faculty members. Post session video recordings will be made available until the programme duration.
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.
