Machine Learning and its Applications

Contents Preface SECTION I: INTRODUCTION Introduction 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 SECTION II: MACHINE LEARNING Data pre-processing Feature extraction Sampling Data transformation Outlier removal Data deduplication Relevance filtering Normalization, discretization and aggregation Entity resolution Supervised learning Classification 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 SECTION III: DEEP LEARNING 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 SECTION IV: LEARNING TECHNIQUES Learning techniques Learning issues Cross-validation Ensemble learning Reinforcement learning Active learning Machine teaching Automated machine learning SECTION V: MACHINE LEARNING APPLICATIONS Machine Learning Applications Anomaly detection Biomedicale applications Natural language processing Other applications Future development Research directions References Index

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• Algoritmos e estruturas de dados
• Engenharia do controlo automático
• Algoritmos e estruturas de dados
• Ciência da Computação