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An Elementary Introduction to Statistical Learning Theory 1st Edition by Sanjeev Kulkarni, Gilbert Harman ISBN 0470641835 9780470641835

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An Elementary Introduction to Statistical Learning Theory 1st Edition Sanjeev Kulkarni Digital Instant Download

Author(s): Sanjeev Kulkarni, Gilbert Harman
ISBN(s): 9780470641835, 0470641835
Edition: 1
File Details: PDF, 2.97 MB
Year: 2011
Language: english
SKU: EB-2608522 Category: Tags: ,

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ISBN 10: 0470641835 
ISBN 13: 9780470641835
Author: Sanjeev Kulkarni, Gilbert Harman

A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference.

Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting.

Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study.

An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.

An Elementary Introduction to Statistical Learning Theory 1st Table of contents:

chapter 1 – foundations of probability and statistics
probability basics: events, random variables, and distributions
expectation, variance, and covariance
common distributions in statistical learning
law of large numbers and central limit theorem

chapter 2 – supervised learning
problem setup: inputs, outputs, and training data
regression and classification tasks
loss functions and risk minimization
empirical risk vs. expected risk

chapter 3 – the bias-variance tradeoff
understanding bias and variance
overfitting and underfitting
model complexity and capacity
cross-validation techniques

chapter 4 – linear methods
linear regression and least squares
linear classification: perceptron and logistic regression
geometric interpretation of linear models
regularization: ridge and lasso

chapter 5 – nonlinear methods
kernel methods and feature spaces
decision trees and ensemble methods
neural networks: an elementary perspective
comparing linear and nonlinear approaches

chapter 6 – generalization and capacity control
vapnik–chervonenkis (vc) dimension
probably approximately correct (pac) learning
structural risk minimization
bounds on generalization error

chapter 7 – model selection and validation
training, validation, and test sets
hyperparameter tuning
performance metrics and evaluation
practical considerations in real data

chapter 8 – unsupervised learning (optional)
clustering and dimensionality reduction
principal component analysis
manifold learning basics
relationship to supervised learning

chapter 9 – practical considerations
data preprocessing and feature engineering
handling missing data
scaling up: large datasets and computation
software tools and libraries

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Tags: Sanjeev Kulkarni, Gilbert Harman, Statistical