A First Encounter with Machine Learning
A First Encounter with Machine Learning
Contents
Preface
iii
MEANT FOR INDUSTRY AS WELL AS BACKGROUND READING]
Learning and Intuition
vii
Data and Information
1.1Data Representation
1.2Preprocessing the Data
Data Visualization
Learning
11
3.1In a Nutshell
Types of Machine Learning
17
4.1In a Nutshell
Nearest Neighbors Classification
21
5.1The Idea In a Nutshell
The Naive Bayesian Classifier
6.1The Naive Bayes Model
6.2Learning a Naive Bayes Classifier
6.3Class-Prediction for New Instances
6.4Regularization
6.5Remarks
6.6The Idea In a Nutshell
The Perceptron
33
7.1The Perceptron Model
7.2A Different Cost function: Logistic Regression
7.3The Idea In a Nutshell
Support Vector Machines
39
wTx=b+δ(8.1)wTx=b−δ(8.2)
8.1The Non-Separable case
Support Vector Regression
47
Kernel ridge Regression
10.1Kernel Ridge Regression
10.2An alternative derivation
Kernel K-means and Spectral Clustering
55
Kernel Principal Components Analysis
59
12.1Centering Data in Feature Space
Fisher Linear Discriminant Analysis
63
13.1Kernel Fisher LDA
Kernel Canonical Correlation Analysis
69
14.1Kernel CCA
Essentials of Convex Optimization
A.1Lagrangians and all that
Appendix B Kernel Design
B.1Polynomials Kernels
B.2All Subsets Kernel
B.3The Gaussian Kernel
Bibliography
Powered by
GitBook
Essentials of Convex Optimization
Essentials of Convex Optimization
results matching "
"
No results matching "
"