Machine and Deep Learning Algorithms and Applications
- Length: 123 pages
- Edition: 1
- Language: English
- Publisher: Morgan & Claypool
- Publication Date: 2021-12-22
- ISBN-10: 1636392679
- ISBN-13: 9781636392677
- Sales Rank: #0 (See Top 100 Books)
This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning to address a variety of needs and applications.
The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.
Preface Acknowledgments Introduction to Machine Learning Brief History Learning Paradigms The Emergence of Deep Learning Relation Between AI, ML, and DL Organization of the Book Supervised Learning Regression vs. Classification Common Regression Algorithms Linear Regression Nonlinear Regression Common Classification Algorithms Logistic Regression Support Vector Machines The K-Nearest Neighbor Algorithm Naive Bayes Classifiers Decision Trees Summary Unsupervised Learning Common Clustering Algorithms The K-means Algorithm Spectral Clustering Gaussian Mixture Models Feature Dimensionality Reduction for Unsupervised ML Principal Component Analysis Independent Component Analysis t-Distributed Stochastic Neighbor Embedding Summary Semi-Supervised Learning Introduction to Semi-Supervised learning Graph-Based Semi-Supervised Learning Matrix Factorization Approaches Random Walk-Based Approaches Graph Neural Networks Positive-Unlabeled Learning Summary Neural Networks and Deep Learning Perceptron: Basic Unit Multi-Layer Perceptron Training Using the Backpropagation Algorithm Activation Functions Neural Network Regularization: Avoiding Over-Fitting Convolutional Neural Networks Convolutional Layer Max-Pooling Layer ConvNet Architecture Recurrent Neural Networks Unsupervised Representation Learning Using Neural Nets Auto-Encoders Generative Adversarial Networks Summary Machine and Deep Learning Applications Sensor Data Analytics Machine Condition Monitoring ML in Image and Vision Autonomous Vehicle Applications Wireless Communications Enabled by ML Text and Natural Language Processing Speech and Audio Data Graph and Relational Data Tiny and Embedded Machine Learning Machine Learning in Healthcare Machine Learning in Energy Applications Machine Learning in Defense and Security Applications Machine Learning for Social Media Machine Learning in Entertainment Machine Learning in Manufacturing Quantum Machine Learning Conclusion and Future Directions Further Reading Bibliography Authors' Biographies
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