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Computer science --- Machine theory --- Formal languages --- Computational complexity --- Automates mathématiques, Théorie des --- Langages formels --- Complexité de calcul (Informatique) --- Computational Complexity --- 510.5 --- #TELE:SISTA --- Abstract automata --- Abstract machines --- Automata --- Mathematical machine theory --- Algorithms --- Logic, Symbolic and mathematical --- Recursive functions --- Robotics --- Formalization (Linguistics) --- Language and languages --- Complexity, Computational --- Electronic data processing --- Algorithms. Computable functions --- Computational complexity. --- Formal languages. --- Machine theory. --- 510.5 Algorithms. Computable functions --- Automates mathématiques, Théorie des --- Complexité de calcul (Informatique) --- Informatique --- Computer science. --- Language --- Automates abstraits

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Formal languages --- Machine theory --- 510.5 --- #TCPW P3.0 --- 681.3*F4 --- Algorithms. Computable functions --- Mathematical logic and formal languages (Theory of computation) --- Formal languages. --- Machine theory. --- 510.5 Algorithms. Computable functions --- 681.3*F4 Mathematical logic and formal languages (Theory of computation)

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This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

Computer Science --- Statistics --- Quantitative research --- Computer science. --- Statistics. --- Quantitative research.

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This book constitutes the refereed proceedings of the 8th International Frontiers of Algorithmics Workshop, FAW 2014, held in Zhangjiajie, China, in June 2014. The 30 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 65 submissions. They provide a focused forum on current trends of research on algorithms, discrete structures, operations research, combinatorial optimization and their applications.

Complex analysis --- Discrete mathematics --- Computer science --- Computer architecture. Operating systems --- Computer. Automation --- complexiteit --- discrete wiskunde --- complexe analyse (wiskunde) --- bedrijfssoftware --- computers --- informatica --- computerkunde