Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan eBook Free Download

 

Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan eBook Free Download

 

Introduction

to

Machine

Learning

 

Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan eBook Free Download

Introduction:

Machine learning contemplates the inquiry “by what means would we be able to assemble PC programs that consequently enhance their execution through experience?” This incorporates figuring out how to perform numerous sorts of assignments in view of numerous sorts of experience. For instance, it incorporates robots figuring out how to better explore roaming so as to take into account experience picked up their surroundings, medicinal choice guides that figure out how to foresee which treatments function best for which infections in view of information mining of verifiable wellbeing records, and discourse acknowledgment frameworks that figure out how to better comprehend your discourse in view of experience listening to you.

This course is intended to give PhD understudies an exhaustive establishing in the techniques, hypothesis, science and calculations expected to do research and applications in machine learning. The points of the course draw from machine learning, established insights, information mining, Bayesian measurements and data hypothesis. Understudies entering the class with a prior working learning of likelihood, insights and calculations will be at favorable position, however the class has been planned so that anybody with a solid numerate foundation can make up for lost time and completely partake.

Contents:

1 Introduction 3

1.1 A Taste of Machine Learning 3

1.1.1 Applications 3

1.1.2 Data 7

1.1.3 Problems 9

1.2 Probability Theory 12

1.2.1 Random Variables 12

1.2.2 Distributions 13

1.2.3 Mean and Variance 15

1.2.4 Marginalization, Independence, Conditioning, and Bayes Rule 16

1.3 Basic Algorithms 20

1.3.1 Naive Bayes 22

1.3.2 Nearest Neighbor Estimators 24

1.3.3 A Simple Classi er 27

1.3.4 Perceptron 29

1.3.5 K-Means 32

2 Density Estimation 37

2.1 Limit Theorems 37

2.1.1 Fundamental Laws 38

2.1.2 The Characteristic Function 42

2.1.3 Tail Bounds 45

2.1.4 An Example 48

3 Optimization 91

3.1 Preliminaries 91

3.1.1 Convex Sets 92

3.1.2 Convex Functions 92

3.1.3 Subgradients 96

3.1.4 Strongly Convex Functions 97

3.1.5 Convex Functions with Lipschitz Continous Gradient 98

3.1.6 Fenchel Duality 98

3.1.7 Bregman Divergence 100

3.2 Unconstrained Smooth Convex Minimization 102

3.2.1 Minimizing an One-Dimensional Convex Function 102

3.2.2 Coordinate Descent 104

4 Online Learning and Boosting 143

4.1 Halving Algorithm 143

4.2 Weighted Majority 144

5 Conditional Densities 149

5.1 Logistic Regression 150

5.2 Regression 151

5.2.1 Conditionally Normal Models 151

5.2.2 Posterior Distribution 151

5.2.3 Heteroscedastic Estimation 151

5.3 Multiclass Classi cation 151

5.3.1 Conditionally Multinomial Models 151

6 Kernels and Function Spaces 155

6.1 The Basics 155

6.1.1 Examples 156

6.2 Kernels 161

6.2.1 Feature Maps 161

6.2.2 The Kernel Trick 161

6.2.3 Examples of Kernels 161

7 Linear Models 165

7.1 Support Vector Classi cation 165

Reference section 1 Linear Algebra and Functional Analysis 197

Reference section 2 Conjugate Distributions 201

Reference section 3 Loss Functions 203

List of sources 221

Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan eBook Free Download

 

Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan eBook Free Download

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