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Statistics for Machine Learning

You're reading from Statistics for Machine Learning Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781788295758

Length 442 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Pratap Dangeti

View More author details

Table of Contents (10) Chapters

Preface

1. Journey from Statistics to Machine Learning FREE CHAPTER

2. Parallelism of Statistics and Machine Learning

3. Logistic Regression Versus Random Forest

4. Tree-Based Machine Learning Models

5. K-Nearest Neighbors and Naive Bayes

6. Support Vector Machines and Neural Networks

7. Recommendation Engines

8. Unsupervised Learning

9. Reinforcement Learning

Temporal difference learning

Temporal Difference (TD) learning is the central and novel theme of reinforcement learning. TD learning is the combination of both Monte Carlo (MC) and Dynamic Programming (DP) ideas. Like Monte Carlo methods, TD methods can learn directly from the experiences without the model of environment. Similar to Dynamic Programming, TD methods update estimates based in part on other learned estimates, without waiting for a final outcome, unlike MC methods, in which estimates are updated after reaching the final outcome only.

Comparison between Monte Carlo methods and temporal difference learning

Though Monte-Carlo methods and Temporal Difference learning have similarities, there are inherent advantages of TD-learning over Monte Carlo methods.

Monte Carlo methods	Temporal Difference learning
MC must wait until the end of the episode before the return is known.	TD can learn online after every step and does not need to wait until the end of episode.
MC has high variance and low...

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Authors (1)

Pratap Dangeti

Pratap Dangeti

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.

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