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The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity.

Research & information: general --- Mathematics & science --- ARIMA model --- time series analysis --- online optimization --- online model selection --- precipitation nowcasting --- deep learning --- autoencoders --- radar data --- generalization error --- recurrent neural networks --- machine learning --- model predictive control --- nonlinear systems --- neural networks --- low power --- quantization --- CNN architecture --- multi-objective optimization --- genetic algorithms --- evolutionary computation --- swarm intelligence --- Heating, Ventilation and Air Conditioning (HVAC) --- metaheuristics search --- bio-inspired algorithms --- smart building --- soft computing --- training --- evolution of weights --- artificial intelligence --- deep neural networks --- convolutional neural network --- deep compression --- DNN --- ReLU --- floating-point numbers --- hardware acceleration --- energy dissipation --- FLOW-3D --- hydraulic jumps --- bed roughness --- sensitivity analysis --- feature selection --- evolutionary algorithms --- nature inspired algorithms --- meta-heuristic optimization --- computational intelligence