Applied AI, ML and Decision Science Programme (Formerly known as Advanced Certificate Programme in Data Science and Decision Science - Batch 06)

• 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

Disclaimer:

• 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.

All examinations and evaluations will be conducted by IIT Delhi's faculty members.

It is upon the faculty's discretion if any re-attempt would be allowed of the quiz or assignment, and the evaluation methodology.

LIVE masterclasses are done by the esteemed faculty members of IIT Delhi who have real-world experience in the domain. The programme is offered by CEP and the organising department for this programme is the Department of Management Studies, IIT Delhi. Doubt-clearing sessions are carried out by the Programme leaders, as they monitor individual student progress.

If, after starting the sessions, you feel that the course is not appropriate for you, you may seek a refund within 15 days from the Programme Start Date. In such cases, 80% of the total fee paid will be refunded. However, the applicable tax amount paid on the fee will not be refundable.

On successful completion based on candidate attendance and performance in assessments of the programme, the participants will be awarded an e-certificate by CEP, IIT Delhi.

Yes. Recordings of LIVE sessions will be available. However, learners are expected to attend most LIVE online sessions, since attendance is required as part of the programme completion requirement.

Learners should plan for a typical weekly commitment of 6–9 hours, which includes: 3 hours/week of LIVE lectures, and ~3–6 hours/week for readings, practice, assignments, and tool-based exercises.