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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

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The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems.

Technology: general issues --- History of engineering & technology --- supervised classification --- independent and non-identically distributed features --- analytical error probability --- empirical risk --- generalization error --- K-means clustering --- model compression --- population risk --- rate distortion theory --- vector quantization --- overfitting --- information criteria --- entropy --- model-based clustering --- merging mixture components --- component overlap --- interpretability --- time series prediction --- finite state machines --- hidden Markov models --- recurrent neural networks --- reservoir computers --- long short-term memory --- deep neural network --- information theory --- local information geometry --- feature extraction --- spiking neural network --- meta-learning --- information theoretic learning --- minimum error entropy --- artificial general intelligence --- closed-loop transcription --- linear discriminative representation --- rate reduction --- minimax game --- fairness --- HGR maximal correlation --- independence criterion --- separation criterion --- pattern dictionary --- atypicality --- Lempel–Ziv algorithm --- lossless compression --- anomaly detection --- information-theoretic bounds --- distribution and federated learning