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Machine learning workout

Machine learning workout

With Exercises and Practicals in MATLAB

In the crowded population of texts on Machine Learning, the present book is unique in the sense that it keeps the spirit of books on basic subjects, like Calculus: its core is made up of many problems with answers, so that the reader can exercise and detect any misunderstanding on time. The book, then, es not for passive reading. It is meant for learning through exercising. Hard workout. Therefore, each chapter presents a set of exercises to be solved “by hand” (think of a desk check) and a strong set of programming tasks to be solved using MATLAB. Being intended for undergraduates, the book does not dive into deep mathematical waters. It is aimed instead to a deep comprehension of the concepts: the mechanics of the algorithms, the structure and geometric representation of the data, the precise evaluation of the results. All by doing it yourself, like in the good old days.
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Libro Impreso ISBN 9789978103944
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Especificaciones por formato:

Impreso

    Estado de la publicación: Activo
    Año de edición: 2019
    Idioma: Inglés
    ISBN-13: 9789978103944
    Número de páginas del contenido principal:
    176 Páginas
    Size(cm): 15.5 x 23 x 0.9
    Peso (kg): 0.3 kg


Holger Ortega Martínez


CONTENTS

 

I GENERALITIES 

CH. 1 MATRICES AND MATLAB 5

        1.1 Theoretical Briefing

        1.2 Exercises

        1.3 Practical in MATLAB

        1.4 Answers to selected exercises

CH. 2 DEFINITIONS AND DATA REPRESENTATION

        2.1 Theoretical Briefing

        2.2 Exercises

        2.3 Practical in MATLAB

        2.4 Answers to selected exercises

CH. 3 CLASSIFICATION PROBLEMS

        3.1 Theoretical Briefing

        3.2 Exercises

        3.3 Practical in MATLAB

        3.4 Answers to selected exercises

CH. 4 REGRESSION PROBLEMS

        4.1 Theoretical Briefing

        4.2 Exercises

        4.3 Practical in MATLAB

        4.4 Answers to selected exercises

II REGRESSION

CH. 5 LINEAR REGRESSION

        5.1 Theoretical Briefing

        5.2 Exercises

        5.3 Practical in MATLAB

        5.4 Answers to selected exercises

CH. 6 OVERFITTING AND UNDERFITTING: VISUALISATION

        6.1 Theoretical Briefing

        6.2 Exercises

        6.3 Practical in MATLAB

        6.4 Answers to selected exercises

III HISTORIC ALGORITHMS

CH. 7 MCCULLOCH-PITTS NEURON

        7.1 Theoretical Briefing

        7.2 Exercises

       7.3 Practical in MATLAB

       7.4 Answers to selected exercises

CH. 8 PERCEPTRON

        8.1 Theoretical Briefing

       8.2 Exercises

       8.3 Practical in MATLAB

       8.4 Answers to selected exercises

CH. 9 ADALINE

       9.1 Theoretical Briefing

       9.2 Exercises

        9.3 Practical in MATLAB

        9.4 Answers to selected exercises

IV CLASSIFICATION ALGORITHMS

CH. 10 NEURAL NETWORKS: FORWARD PROPAGATION

        10.1 Theoretical Briefing

        10.2 Exercises

        10.3 Practical in MATLAB

        10.4 Answers to selected exercises

CH. 11 NEURAL NETWORKS: VALIDATION AND TEST

        11.1 Theoretical Briefing

        11.2 Exercises

        11.3 Practical in MATLAB

        11.4 Answers to selected exercises

CH. 12 NEURAL NETWORK PATTERN RECOGNITION APP

        12.1 Theoretical Briefing

        12.2 Exercises

        12.3 Practical in MATLAB

        12.4 Answers to selected exercises

CH. 13 SUPPORT VECTOR MACHINES

        13.1 Theoretical Briefing

        13.2 Exercises

        13.3 Practical in MATLAB

        13.4 Answers to selected exercises

CH. 14 K-NEAREST NEIGHBOURS

        14.1 Theoretical Briefing

        14.2 Exercises

        14.3 Practical in MATLAB

        14.4 Answers to selected exercises

V PROBLEMS IN IMPLEMENTATION TIME

CH. 15 FEATURE SCALING

        15.1 Theoretical Briefing

        15.2 Exercises

        15.3 Practical in MATLAB

        15.4 Answers to selected exercises

CH. 16 LEARNING CURVES

        16.1 Theoretical Briefing

        16.2 Exercises

        16.3 Practical in MATLAB

        16.4 Answers to selected exercises

CH. 17 PRINCIPAL COMPONENT ANALYSIS

       17.1 Theoretical Briefing

       17.2 Exercises

       17.3 Practical in MATLAB

       17.4 PRACTICAL IN MATLAB

       17.5 Answers to selected exercises

VI UNSUPERVISED LEARNING

CH. 18 K-MEANS ALGORITHM

       18.1 Theoretical Briefing

       18.2 Exercises

       18.3 Practical in MATLAB

       18.4 Answers to selected exercises 


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