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Quick Facts

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

Course Overview

Reinforcement learning is a machine learning method that helps to understand how the software agents should take actions according to the situations and requirements. Helps solve all the problems that have large or high dimensional and potential limitless spaces available. It helps one to investigate how policy prediction and evaluation methods like TD and Monte Carlo can be used to help function approximation settings. 

Prediction and Control with Function Approximation certification course by Coursera include features construction techniques for representational learning through a neutral network, RL and backdrop. This course is designed for the candidates who have completed the first and the second part of the reinforcement learning and specialization training as it is a continuation of the same.

By doing the Prediction and Control with Function Approximation training course they will be a field ready and gain knowledge that will help them master their skills and help upgrade their portfolio which will, in the end, help them towards their successful career. 

The Highlights

  • Approx. 21 hours course
  • 100% online learning
  • Flexible deadlines
  • Favourable for candidates with intermediate knowledge.
  • 7 days of free trial available.
  • Offered by the University of Alberta

Programme Offerings

  • quizzes
  • Readings
  • assignments
  • Projects

Courses and Certificate Fees

Certificate AvailabilityCertificate Providing Authority
yesCoursera

The Prediction and Control with Function Approximation certification fee details :

Particulars

Fees (per month)

Course Fee, 1 Month

Rs. 6,587

Course Fee, 3 Months

Rs. 13,174

Course Fee, 6 Months

Rs. 19,761


Eligibility Criteria

Work experience

The candidate should have experience of working with python for at least a year.

Education

The candidates are expected to complete the first two parts of the reinforcement learning specialization course 

Certification qualification details

Candidates should successfully attend and complete the entire programme to get the Prediction and Control with Function Approximation certification by Coursera.

What you will learn

Machine learningKnowledge of Artificial Intelligence

After completion of the Prediction and Control with Function Approximation certification syllabus, the candidates will learn to:

  • They will be able to start implementing the policy gradient method also called the Actor-Critic method on a discrete state environment
  • They will learn the implementation of TD effectively
  • They will learn the use of supervised learning approaches using machine learning.
  • They will understand all the difficulties that come across while moving to function approximation.

Who it is for

With the Prediction and Control with Function Approximation course, the candidates who have an interest for pursuing careers as Python Programmers, and  ML Engineers.


Application Details

Candidates need to follow a set process for the Prediction and Control with Function Approximation classes admission process

Step 1: Candidates need to apply on the programme URL https://www.coursera.org/learn/prediction-control-function-approximation

Step 2: Select the tab Enroll for free. The 7-day free trial will be given once the candidate signs up.

Step 3: As 7 days come to an end, the candidate needs to make a monthly fee paid to access the programme again.

The Syllabus

Videos
  • Meet your instructors
  • Course 3 Introduction
Readings
  • Reinforcement Learning Textbook
  • Read Me: Pre-requisites and Learning Objectives

Videos
  • Generalization and Discrimination
  • Moving to Parameterized Functions
  • Framing Value Estimation as Supervised Learning
  • Introducing Gradient Descent
  • The Value Error Objective
  • Gradient Monte for Policy Evaluation
  • Semi-Gradient TD for Policy Evaluation
  • State Aggregation with Monte Carlo
  • Comparing TD and Monte Carlo with State Aggregation
  • Week 1 Summary
  • The Linear TD Update
  • Doina Precup: Building Knowledge for AI Agents with Reinforcement Learning
  • The True Objective for TD
Readings
  • Weekly Reading: On-policy Prediction with Approximation
  •  Module 1 Learning Objectives1
Practice exercise
  • On-policy Prediction with Approximation

Videos
  • Generalization Properties of Coarse Coding
  • Coarse Coding
  • Tile Coding
  • What is a Neural Network?
  • Using Tile Coding in TD
  • Non-linear Approximation with Neural Networks
  • Optimization Strategies for NNs
  • Gradient Descent for Training Neural Networks
  • David Silver on Deep Learning + RL = AI?
  • Deep Neural Networks
  • Week 2 Review
Readings
  • Weekly Reading: On-policy Prediction with Approximation 
  • Module 2 Learning Objectives
Practice exercise
  • Constructing Features for Prediction

Videos
  • Episodic Sarsa in Mountain Car
  • Episodic Sarsa with Function Approximation
  • Expected Sarsa with Function Approximation
  • Average Reward: A New Way of Formulating Control Problems
  • Exploration under Function Approximation
  • Satinder Singh on Intrinsic Rewards
  • Week 3 Review
Readings
  • Weekly Reading: On-policy Control with Approximation 
  • Module 3 Learning Objectives
Practice exercise
  • Control with Approximation

Videos
  • Advantages of Policy Parameterization
  • Learning Policies Directly
  • The Objective of Learning Policies
  • Estimating the Policy Gradient
  • The Policy Gradient Theorem
  • Actor-Critic Algorithm
  • Gaussian Policies for Continuous Actions
  • Demonstration with Actor-Critic
  • Congratulations! Course 4 Preview
  • Actor-Critic with Softmax Policies
  • Week 4 Summary
Readings
  • Weekly Reading: Policy Gradient Methods 
  • Module 4 Learning Objectives
Practice exercise
  • Policy Gradient Methods

Instructors

University of Alberta, Edmonton Frequently Asked Questions (FAQ's)

1: If I want a refund for this programme, can I apply for it?

The candidate can access the programme for free for 7 days. Hence the fee will not be refunded once paid.

2: How much time is required for this programme?

The programme requires 22 hours to complete. 

3: What are some of the skills that I will master during the Prediction and Control with Function Approximation online course?

The candidate will learn a series of skills namely, Intelligent Systems, Reinforcement Learning, Function Approximation, Machine Learning and many others. 

4: Will I get a certificate after I complete this programme?

Yes, the candidates will be awarded certificates once the programme is completed successfully. 

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