1st Edition

Machine Learning and its Applications

By Peter Wlodarczak Copyright 2020
    204 Pages 5 Color & 47 B/W Illustrations
    by CRC Press

    204 Pages 5 Color & 47 B/W Illustrations
    by CRC Press

    204 Pages 5 Color & 47 B/W Illustrations
    by CRC Press

    In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge.

    This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general.

    This book is meant to be an introduction into machine learning. It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book.

    Key Features:

  • Describes real world problems that can be solved using Machine Learning

  • Provides methods for directly applying Machine Learning techniques to concrete real world problems

  • Demonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R
  • Contents




    Data mining

    Data mining steps

    Data collection

    Data pre-processing

    Data analysis

    Data post-processing

    Machine learning basics

    Supervised learning

    Unsupervised learning

    Semi-supervised learning

    Function approximation

    Generative and discriminative models

    Evaluation of learner


    Data pre-processing

    Feature extraction


    Data transformation

    Outlier removal

    Data deduplication

    Relevance filtering

    Normalization, discretization and aggregation

    Entity resolution

    Supervised learning


    Regression analysis

    Logistic regression

    Evaluation of learner

    Evaluating a learner

    Unsupervised learning

    Types of clustering

    k-means clustering

    Hierarchical clustering

    Visualizing clusters

    Evaluation of clusters

    Semi-supervised learning

    7.1 Expectation maximization

    7.2 Pseudo labeling


    Deep Learning

    8.1 Deep Learning Basics

    8.2 Convolutional neural networks

    8.3 Recurrent neural networks

    8.4 Restricted Boltzmann machines

    8.5 Deep belief networks

    8.6 Deep autoencoders


    Learning techniques

    Learning issues


    Ensemble learning

    Reinforcement learning

    Active learning

    Machine teaching

    Automated machine learning


    Machine Learning Applications

    Anomaly detection

    Biomedicale applications

    Natural language processing

    Other applications

    Future development

    Research directions





    Peter Wlodarczak is an IT consultant in Data Analytics and Machine Learning. Born in Basel, Switzerland, he holds a Master degree and a PhD from the University of Southern Queensland, Australia. He has many years of experience in large software engineering and data analysis projects. He has published more than 20 papers and book chapters in this area and has presented his work on many conferences. His research interests include among other Machine Learning, eHealth and Bio computing.