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Decision Making and Reinforcement Learning
Master sequential decision making and reinforcement learning. Learn dynamic programming, Monte Carlo methods, and temporal difference learning.
Course Cost
₹ 2,699
Intermediate
Skill Level
42 Hours
Self-paced lessons
This comprehensive course introduces sequential decision making and reinforcement learning fundamentals. Students explore utility theory, multi-armed bandit problems, and Markov decision processes (MDPs). The curriculum covers dynamic programming algorithms, partial observability concepts, and reinforcement learning paradigms including Monte Carlo methods and temporal difference learning. Through hands-on programming assignments and theoretical study, learners develop practical skills in implementing key algorithms and understanding their applications.
What you'll learn
Map between qualitative preferences and appropriate quantitative utilities
Model sequential decision problems using multi-armed bandits and Markov processes
Implement dynamic programming algorithms for optimal policies
Master Monte Carlo and temporal difference learning methods
Understand partial observability in real-world problems
Develop practical skills in reinforcement learning implementation
Skills you'll gain
This course includes:
264 Minutes PreRecorded video
8 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 8 modules in this course
This course provides a comprehensive introduction to sequential decision making and reinforcement learning. Students begin with utility theory foundations and progress through multi-armed bandit problems, Markov decision processes, and dynamic programming solutions. The curriculum covers both theoretical concepts and practical implementations, including partial observability, Monte Carlo methods, and temporal difference learning. Through eight modules, learners develop skills in implementing various reinforcement learning algorithms while understanding their mathematical foundations and real-world applications.
Decision Making and Utility Theory
Module 1 · 5 Hours to complete
Bandit Problems
Module 2 · 4 Hours to complete
Markov Decision Processes
Module 3 · 4 Hours to complete
Dynamic Programming
Module 4 · 7 Hours to complete
Partially Observable Markov Decision Processes
Module 5 · 5 Hours to complete
Monte Carlo Methods
Module 6 · 4 Hours to complete
Temporal-Difference Learning
Module 7 · 8 Hours to complete
Reinforcement Learning - Generalization
Module 8 · 5 Hours to complete
Fee Structure
Payment options
Financial Aid
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
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Frequently asked Questions
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