Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Building dynamic pricing systems with reinforcement learning—from demand modeling and simulators to training bandit/RL agents for revenue optimization in platforms and two-sided markets, with practical guardrails for responsible deployment. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
A hands-on course on building dynamic pricing systems with reinforcement learning—from demand modeling and simulators to training bandit/RL agents for revenue optimization in platforms and two-sided markets, with practical guardrails for responsible deployment.
📋 Course Objectives
1. Apply AI Enablement methods to authentic research and industry problems.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
The Pricing Problem
• Price elasticity, willingness to pay and the revenue-against-volume trade-off
• Dynamic, personalised and segmented pricing distinguished
• Two-sided markets where a price change moves both supply and demand
Modelling and Simulation
• Estimating demand curves from observational data and the endogeneity problem
• Building a pricing simulator and validating it against held-out periods
• Seasonality, competitor response and effects the simulator will miss
Learning Prices Online
• Multi-armed and contextual bandits as the natural first formulation
• Thompson sampling and UCB, and regret as the performance measure
• When the problem genuinely needs full RL instead of a bandit
Reinforcement Learning for Revenue
• State design from inventory, time and demand signals
• Reward shaping for long-horizon revenue rather than immediate conversion
• Offline evaluation before any agent touches live traffic
Deploying Responsibly
• Price floors, ceilings and rate limits as hard constraints on the agent
• Algorithmic collusion risk and the competition law exposure it creates
• Fairness, price discrimination law and customer trust when pricing is visible
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
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