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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video 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 Availability	Certificate Providing Authority
yes	Coursera

The Prediction and Control with Function Approximation certification fee details :

Particulars	Fees (per month)
Course Fee, 1 Month	Rs. 6,486
Course Fee, 3 Months	Rs. 12,973
Course Fee, 6 Months	Rs. 19,459

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

Python ProgrammerMachine Learning Engineer

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.