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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf StudyVideo and Text Based

Course Overview

The Practical Reinforcement Learning programme by Coursera is the fourth out of the seven courses included in the ‘Advanced Machine Learning Specialization’. Offered by the National Research University- Higher School of Economics, this online programme will make you an expert in the field of machine learning.

Reinforcement Learning is one of the fundamentals of machine learning. Without getting a good grasp of this particular area, one cannot master the art of artificial intelligence. With the certification course in Practical Reinforcement Learning by Coursera, you can get in-depth knowledge about Reinforcement Learning and hone the various skills required to master this area of machine learning.

The Practical Reinforcement Learning online course by Coursera is well equipped with a planned curriculum as well as offerings like graded quizzes and assignments, peer feedbacks, practice quizzes, etc. that will help you to become proficient in this field. You will earn a shareable certificate upon completion as well.

The Practical Reinforcement Learning online programme by Coursera is a full package that covers all the core concepts of Reinforcement Learning, including free methods, dynamic programming, value-based methods, neural networks, algorithm-writing, model-free methods, and more.

The Highlights

  • Full course specialization
  • Course videos and readings
  • 100% online
  • Self-paced learning 
  • Shareable certificate
  • Completion in approximately 26 hours
  • Offered by National Research University - Higher School of Economics

Programme Offerings

  • Online videos and readings
  • Graded Quizzes
  • Grades assignments
  • Programming Assignments
  • Full Specialization
  • Self-paced learning
  • peer feedback

Courses and Certificate Fees

Certificate AvailabilityCertificate Providing Authority
yesCoursera

Interested applicants can opt for a 7-day free trial and enrol for free. To avail full contents of the course along with the certificate, candidates need to purchase the experience certificate.


What you will learn

Programming skillsKnowledge of AlgorithmsMachine learningData science knowledgeKnowledge of Artificial Intelligence

Once you complete the Practical Reinforcement Learning program, you will:

  • Build a strong foundation of reinforcement learning methods like value/policy iteration, policy, gradient, q-learning, etc.
  • Understand the functioning and usage of deep neural networks for tasks in reinforcement learning, also called the “hype train”
  • Acquire skills to write and develop state-of-the-art algorithms that make the machines behave in the desired way
  • Know how to teach neural networks to play games through reinforcement learning, and also use it for contextual bandits and seq2seq

Who it is for


Admission Details

Follow the steps given below to take admission in the Practical Reinforcement Learning program by Coursera :

  • Visit the Course page.
  • On the website, glide your cursor over the “Explore” button. Under the “Data Science” category, choose “Machine Learning”.
  • This will display all the courses offered in Machine Learning. Choose the Course you want to take admission. In this case, choose “Practical Reinforcement Learning”.
  • You will be redirected to the course-specific webpage. Locate the “Enroll for Free” button and click on it.
  • Sign up using your Email ID and password. If you already have an account, use the details to log in. You will get a 7-day free trial once you enrol.
  • To get the full course offerings and continue after the free trial, you will have to purchase the certificate experience. You can choose your preferred payment to do so.

Application Details

There is no additional application form for the course. Interested candidates are required to sign up using their Email ID and password, or their Google, Facebook, or Apple accounts.

The Syllabus

  • About the University
  • Why should you care
  • More on the approximate cross-entropy method
  • Reinforcement learning vs all
  • Approximate cross-entropy method
  • Multi-armed bandit
  • Decision process & applications
  • Markov Decision Process
  • Cross-entropy method
  • Evolution strategies: the core idea
  • Evolution strategies: duct tape
  • Evolution strategies: math problems
  • Evolution strategies: log-derivative trick
  • Blackbox optimization: drawbacks

  • Measuring Policy Optimality
  • Policy: evaluation & improvement
  • Policy and value iteration
  • State and Action Value Functions
  • Reward design

  • Model-based vs model-free
  • On-policy vs off-policy; Experience replay
  • Monte-Carlo & Temporal Difference; Q-learning
  • Footnote: Monte-Carlo vs Temporal Difference
  • Exploration vs Exploitation
  • Accounting for exploration. Expected Value SARSA

  • Supervised & Reinforcement Learning
  • Partial observability
  • Double Q-learning
  • Loss functions in value-based RL
  • Difficulties with Approximate Methods
  • DQN – bird's eye view
  • DQN – the internals
  • DQN: statistical issues
  • More DQN tricks

  • Intuition
  • All Kinds of Policies
  • Policy gradient formalism
  • The log-derivative trick
  • REINFORCE
  • Advantage actor-critic
  • Duct tape zone
  • Policy-based vs Value-based
  • Case study: A3C
  • Combining supervised & reinforcement learning

  • Recap: bandits
  • Regret: measuring the quality of exploration
  • The message just repeats. 'Regret, Regret, Regret’.
  • Intuitive explanation
  • Thompson Sampling
  • Optimism in the face of uncertainty
  • UCB-1
  • Bayesian UCB
  • Introduction to planning
  • Monte Carlo Tree Search

HSE University Frequently Asked Questions (FAQ's)

1: When will I get access to the classes?

Once you enrol for the course and make the necessary payments, you will get access to all the reading material as well as assignments that will help you to earn a certificate later on.

2: What all is included with the subscription for this course?

On enrolment, you will be provided with the utmost assistance to complete this course successfully. You will get access to all the video lectures, reading materials, quizzes, and assignments. On completion, you will also get an electronic certificate that is shareable on all platforms.

3: Is there any scholarship available for this course?

Yes, Coursera makes sure that nothing comes between you and knowledge. Therefore, financial aid is available. The details are available on the course-specific website.

4: Is there a refund policy?

Upon successful subscription, students will be given a 7-day free trial of the course during which you can cancel without any penalty. After the trial ends, you can cancel the subscription anytime, but there will not be any refunds.

5: Is this a 100% online course, or do I need to attend classes in person?

This course is a 100% online course. Therefore there is no need to attend classes physically. You can access reading materials and videos anytime and anywhere on your device.

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