Applied Machine Learning
Authors: David Forsyth
ISBN-10: 3030181138
ISBN-13: 9783030181130
Edition 版次: 1st ed. 2019
Publication Date 出版日期: 2019-07-13
Print Length 页数: 494 pages
9
Book Description
By finelybook
Machine learning methods are now an important tool for scientists,researchers,engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning,but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning,this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done,with a strong emphasis on using existing tools and packages,rather than writing one’s own code.
A companion to the author’s Probability and Statistics for Computer Science,this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).
Emphasizing the usefulness of standard machinery from applied statistics,this textbook gives an overview of the major applied areas in learning,including coverage of:
classification using standard machinery (naive bayes; nearest neighbor; SVM)
clustering and vector quantization (largely as in PSCS)
PCA (largely as in PSCS)
variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)
linear regression (largely as in PSCS)
generalized linear models including logistic regression
model selection with Lasso,elasticnet
robustness and m-estimators
Markov chains and HMM’s (largely as in PSCS)
EM in fairly gory detail; long experience teaching this suggests one detailed example is required,which students hate; but once they’ve been through that,the next one is easy
simple graphical models (in the variational inference section)
classification with neural networks,with a particular emphasis on image classification
autoencoding with neural networks
structure learning
Part .Classification
1.Learning to Classify
2.SVMs and Random Forests
3.A Little Learning Theory
Part ll.High Dimensional Data
4.High Dimensional Data
5.Principal Component Analysis
6.Low Rank Approximations
7.Canonical Correlation Analysis
Part ll.Clustering
8.Clustering
9.Clustering Using Probability Models
Part lV.Regression
10.Regression
11.Regression: Choosing and Managing Models
12.Boosting
Part V.Graphical Models
13.Hidden Markov Models
14.Learning Sequence Models Discriminatively
15.Mean Field Inference
Part VI.Deep Networks
16.Simple Neural Networks
17.Simple lmage Classifhers
18.Classifying Images and Detecting Objects
19.Small Codes for Big Signals
