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  • Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

    Data-Driven Science and Engineering by Brunton, Steven L.; Kutz, J. Nathan;

    Machine Learning, Dynamical Systems, and Control

      • 10% KEDVEZMÉNY?

      • A kedvezmény csak az 'Értesítés a kedvenc témákról' hírlevelünk címzettjeinek rendeléseire érvényes.
      • Kiadói listaár GBP 51.99
      • Az ár azért becsült, mert a rendelés pillanatában nem lehet pontosan tudni, hogy a beérkezéskor milyen lesz a forint árfolyama az adott termék eredeti devizájához képest. Ha a forint romlana, kissé többet, ha javulna, kissé kevesebbet kell majd fizetnie.

        24 838 Ft (23 655 Ft + 5% áfa)
      • Kedvezmény(ek) 10% (cc. 2 484 Ft off)
      • Kedvezményes ár 22 354 Ft (21 290 Ft + 5% áfa)

    24 838 Ft

    Beszerezhetőség

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    Why don't you give exact delivery time?

    A beszerzés időigényét az eddigi tapasztalatokra alapozva adjuk meg. Azért becsült, mert a terméket külföldről hozzuk be, így a kiadó kiszolgálásának pillanatnyi gyorsaságától is függ. A megadottnál gyorsabb és lassabb szállítás is elképzelhető, de mindent megteszünk, hogy Ön a lehető leghamarabb jusson hozzá a termékhez.

    Rövid leírás:

    This beginning graduate textbook teaches data science and machine learning methods for modeling, prediction, and control of complex systems.

    Több

    Hosszú leírás:

    Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.

    'This is a very timely, comprehensive and well written book in what is now one of the most dynamic and impactful areas of modern applied mathematics. Data science is rapidly taking center stage in our society. The subject cannot be ignored, either by domain scientists or by researchers in applied mathematics who intend to develop algorithms that the community will use. The book by Brunton and Kutz is an excellent text for a beginning graduate student, or even for a more advanced researcher&&&160;interested in this field. The main theme seems to be applied optimization. The subtopics include dimensional reduction, machine learning, dynamics and control and reduced order methods. These were well chosen and well covered.' Stanley Osher, University of California

    Több

    Tartalomjegyzék:

    Part I. Dimensionality Reduction and Transforms: 1. Singular value decomposition; 2. Fourier and wavelet transforms; 3. Sparsity and compressed sensing; Part II. Machine Learning and Data Analysis: 4. Regression and model selection; 5. Clustering and classification; 6. Neural networks and deep learning; Part III. Dynamics and Control: 7. Data
    -driven dynamical systems; 8. Linear control theory; 9. Balanced models for control; 10. Data
    -driven control; Part IV. Reduced
    -Order Models: 11. Reduced
    -order models (ROMs); 12. Interpolation for parametric ROMs.

    Több
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