Applied AI, ML & Decision Science Programme

• 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

• Analysing large structured datasets using supervised and unsupervised algorithms with no-code tools or Python. This includes hyperparameter tuning and model performance evaluation via cross-validatio

• 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

• A case-study-based project where participants must provide business solutions using Python, Excel, or LINGO.

Decision Science is the discipline that focuses on making better, structured decisions using data, analytical models, and quantitative methods. While AI and Machine Learning generate predictions and insights, Decision Science ensures these insights are translated into real‑world actions. In today’s AI‑driven business environment, organisations are not limited by their ability to build models—they are limited by their ability to make the right decisions using those models. Decision Science addresses this gap by combining predictive analytics, optimisation, simulation, and business context to drive measurable outcomes. The Applied AI, Machine Learning and Decision Science Programme by IIT Delhi is among the very few programmes designed to build this complete, end‑to‑end capability, enabling professionals to move beyond insights to real impact.

AI and Machine Learning can identify patterns and generate predictions—but they do not decide what actions to take. Decision Science bridges this gap by helping professionals interpret AI outputs, evaluate trade-offs, and make optimal decisions in real business scenarios.For example:

• AI predicts demand → Decision Science helps optimise supply and inventory

• ML identifies customer segments → Decision Science helps allocate resources effectively

• Models generate insights → Decision frameworks convert them into strategic actions

Batch 6 of the Applied AI, Machine Learning and Decision Science Programme from IIT Delhi combines these capabilities into a single, integrated learning journey. Unlike most programmes that focus only on AI or analytics, this programme emphasises decision-making depth alongside AI capability, making it one of the few offerings in India that brings Applied AI, ML and Decision Science together at this level of rigour and application.

The Applied AI, ML and Decision Science Programme from IIT Delhi covers a comprehensive blend of Applied AI, Machine Learning, Generative AI, Agentic AI and decision‑science techniques. Learners work with AI‑ready data ecosystems, supervised and unsupervised ML algorithms, Deep Learning, NLP, computer vision and LLM‑based Generative AI workflows including transformers, RAG and agentic reasoning models. The programme also teaches predictive analytics, time‑series forecasting, optimisation techniques, simulation, linear and goal programming, and multi‑criteria decision‑making frameworks, providing a complete foundation for business decision science, predictive analytics certification, and AI/ML skills for working professionals.

Yes. The Applied AI, ML and Decision Science Programme is delivered entirely through live, interactive online sessions taught by IIT Delhi faculty members. Classes are primarily held on Saturdays from 9:30 AM to 1:00 PM, with occasional additional sessions scheduled in the afternoon when deeper coverage or extended discussions are required. A detailed session calendar will be shared with all participants well in advance to help them plan their weekly learning schedule.

Learners should expect to dedicate 6–9 hours per week to the Applied AI, ML and Decision Science Programme. This includes around 3 hours of live faculty‑led sessions and an additional 3–6 hours of self-study, such as readings, tool-based exercises, practice assignments, and preparation for quizzes. This structure ensures that learners can apply concepts gradually while balancing professional commitments.

Yes. Participants will have access to recordings of all live online sessions throughout the duration of the Applied AI, ML and Decision Science Programme. This allows learners to revisit lectures for revision or catch up when needed. However, since attendance contributes to programme completion requirements, learners are expected to attend most live sessions to meet academic criteria.

Absolutely. The programme is built specifically for working professionals, business leaders, analysts, consultants, and technology managers who want to future-proof their careers in the AI-first economy. With live weekend sessions led by IIT Delhi faculty, practical assignments, and flexible online learning, professionals can build high-demand AI and decision intelligence skills without stepping away from their current roles.

No prior coding experience is required. The programme follows a balanced code and no-code AI learning approach, making it suitable for both technical and non-technical professionals. Participants learn foundational AI concepts through guided Python applications, intuitive no-code AI tools, machine learning workflows, and real-world business use cases designed for practical understanding rather than purely theoretical learning.

Upon meeting the academic requirements—namely a minimum of 50% attendance and 50% aggregate marks across quizzes and the capstone project—participants will receive a Certificate of Completion issued by CEP, IIT Delhi. Learners who maintain at least 40% attendance but score below 50% overall will receive a Certificate of Participation. All certificates are formally issued through IIT Delhi’s Continuing Education Programme (CEP).

All academic evaluations in the Applied AI, ML and Decision Science Programme, including quizzes, assignments, examinations and capstone projects, are conducted by IIT Delhi faculty members. Evaluation follows IIT Delhi’s CEP academic standards, and faculty have full discretion regarding grading, assessment structure and any reattempt permissions. This ensures rigorous and consistent academic oversight across both Applied AI and Decision Science components.

Learners gain hands-on exposure to industry-standard tools and technologies such as Python, Tableau, Orange, Excel Solver and LINGO, along with modern Generative AI and no‑code AI platforms. These tools are used extensively in assignments, practice labs, and capstone projects, allowing participants to build practical skills across machine learning, predictive analytics, optimisation, and decision intelligence.

Eligibility for the Applied AI, ML and Decision Science Programme includes being a Graduate or Diploma holder (10+2+3) in any discipline. Preference is given to applicants with backgrounds in Science, Technology, Engineering, Mathematics, Management, or related quantitative fields, as the coursework involves mathematical reasoning and analytical thinking. A basic foundation in quantitative subjects helps learners fully benefit from the programme’s AI, ML and decision-science curriculum

Yes. The programme is designed to help professionals transition into high-growth AI, Generative AI, Machine Learning, analytics, and AI-enabled decision-making roles. Learners build hands-on capability in AI model development, LLM applications, predictive analytics, optimisation frameworks, and data-driven business problem-solving — skills increasingly required across digital transformation, AI consulting, product strategy, operations, analytics, and technology leadership functions.

Unlike many online AI programmes that focus only on coding, theory, or isolated GenAI modules, IIT Delhi’s programme follows a distinctive dual-pillar approach that combines Applied AI with Decision Science. Participants learn the complete AI lifecycle — from AI-ready data preparation and machine learning model building to Generative AI, Agentic AI, optimisation, forecasting, and strategic decision-making. The programme emphasises live IIT Delhi faculty-led learning, hands-on business applications, industry-grade tools, and real-world implementation capability.