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Course Details

Course Commencement
Course Completion
Language
English
Exam Dates
Credits & LTP
Each course offers 5 credit points.
Lecture
3
Tutorial
1
Practical (Hands-on)
1
Credit
5
Recommended For:
Graduation Programme - B.E./B.Tech
Branch - Computer Science Engineering and Information Technology
Semester -
5th,  6th,  7 th  and  8th

Course Summary

Reinforcement Learning (RL) is a field of Machine Learning that is concerned with how intelligent agents should act in an environment, so as to maximise the notion of cumulative reward. Generally, a Reinforcement Learning agent can perceive its environment, interpret it, and take action, as well as learn through trial and error. Reinforcement Learning, along with supervised learning and unsupervised learning, is one of the three basic paradigms used in Machine Learning.

According to a report by GlobeNewswire, the global Machine Learning and Reinforcement Learning market was valued at US$ 9.9 billion in 2019, and is projected to reach US$ 14.7 billion by 2025, growing at a Compound Annual Growth Rate (CAGR) of 6.5% between 2020 and 2025. The major driving factors in Machine Learning and Reinforcement Learning market are the increasing need for business strategy planning, machine learning and data processing, to create training systems that provide custom instructions and materials according to need, as well as in robotics and aircraft control.

Reinforcement Learning is a course that provides the methods and procedures to solve very complex problems, which cannot be solved by conventional techniques. The methods of Reinforcement learning are preferred for achieving long-term results, which otherwise can be a very difficult goal to achieve. The Reinforcement Learning method is based on human learning. This course is useful for those interested in learning Artificial Intelligence using Reinforcement Learning methods.

Hands-On

A virtual hands-on environment is integrated within the course.

Students will have to leverage this environment to complete the industry assignment as well as to complete Part B of the summative assessment.

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RECOMMENDED PRIOR KNOWLEDGE
1. Basic Python Programming

2. Basic knowledge of Artificial Intelligence and Machine Learning

Course Syllabus

The course syllabus will be delivered through a combination of eLearning resources, digital lectures, community based digital classrooms as applicable.
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RELATED COURSES

Course Components

Digital Learning
Resources
Courses to consist of enriching eLearning resources View Sample
Digital
Lectures
Multiple digital lectures delivered by a renowned academician and an industry expert, through the entire duration of a course View Sample
Academic Connect
Community
Moderated by an academic expert with a focus on building conceptual clarity View Sample
Industry Connect
Community
Moderated by an industry expert with a focus on building industry oriented applied knowledge View Sample
Industry
Assignment
Access to industry related mini projects to enable practical exposure for candidates View Sample
Summative Assessment
/TCS iON NQT
Candidates to appear for either summative assessments consisting of two parts - Test of Knowledge and Test of Application or TCS iON NQT View Sample
Verifiable Digital
Certificate
Successful candidates to receive a digital certificate, verifiable through online platforms View Sample
Internship
Opportunity
Internship opportunity for toppers in the respective courses, subject to vacancies in corporates and their hiring policies View Sample
Job
Visibility
Get visibility to job vacancies with leading corporate recruiters that recognise the TCS iON NQT certification, subject to vacancies in corporates and their hiring policies View Sample

Testimonials

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FAQs