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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.

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

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Challenging problems arise in all segments of energy industries—generation, transmission, distribution and consumption. Optimization models and methods play a key role in offering decision/policy makers better information to assist them in making sounder decisions at different levels, ranging from operational to strategic planning.

mixed integer linear programming --- fuzzy set theory --- stochastic programming --- mixed integer linear programing --- variable renewable power --- generation efficiency --- optimization --- flexibility option --- portfolio analysis --- firefighting --- semi-mean-absolute deviation model --- component outage --- energy network --- predicted mean vote (PMV) --- generation expansion planning --- building microgrid --- demand side management --- stochastic robust optimization --- oil storage plants --- long-term forecasting --- multi-criteria decision making (MCDM) --- life cycle cost --- graph theory --- scenario-based multistage stochastic programming --- optimal power generation mix --- heating ventilation and air-conditioning (HVAC) --- intermittent sources --- electric-power structure adjustment --- technique for the order of preference by similarity to the ideal solution (TOPSIS) --- integrated energy system --- Markov chain Monte Carlo --- nondominated sorting genetic algorithm (NSGA) --- domino effect --- energy system management model --- electrical distribution systems --- microgrid operation --- influence diagram --- net demand --- wind power forecasting --- energy conservation and emissions reduction --- feasible operation region --- meshed topology --- occupancy-based control --- islanded microgrids --- combined heat and power --- multi-objective optimization --- re-optimization and rescheduling