Info
Communication Principles for Data Science [[electronic resource] /] / by Changho Suh
| Communication Principles for Data Science [[electronic resource] /] / by Changho Suh |
| Autore | Suh Changho |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (294 pages) |
| Disciplina | 381 |
| Collana | Signals and Communication Technology |
| Soggetto topico |
Artificial intelligence—Data processing
Digital media Computer science—Mathematics Mathematical statistics Signal processing Data Science Digital and New Media Probability and Statistics in Computer Science Signal, Speech and Image Processing |
| ISBN | 981-19-8008-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Acknowledgements -- Part 1. Communication over the Gaussian channel -- Chapter 1.Overview of the book -- Chapter 2. A statistical model for additive noise channels -- Chapter 3. Additive Gaussian noise model -- Problem Set 1 -- Chapter 4. Optimal receiver: maximum A Posteriori (MAP) principle -- Chapter 5. Analysis of error probability -- Chapter 6. Multiple bits transmission via pulse amplitude modulation -- Problem Set 2 -- Chapter 7. Multi-shot communication -- Chapter 8. Repetition coding -- Chapter 9: Capacity of the additive white Gaussian noise channel -- Problem Set 3 -- Part 2. Communication over inter-symbol interference (ISI) channels -- Chapter 10. Signal conversion from discrete to continuous time (1/2) -- Chapter 11. Signal conversion from discrete to continuous time (2/2) -- Chapter 12. Optimal receiver architecture -- Problem Set 4 -- Chapter 13. Optimal receiver in ISI channels: maximum likelihood (ML) sequence detection -- Chapter 14. Optimal receiver in ISI channels: Viterbi algorithm -- Problem Set 5 -- Chapter 15.Orthogonal frequency division multiplexing (1/3) -- Chapter 16. Orthogonal frequency division multiplexing (2/3) -- Chapter 17. Orthogonal frequency division multiplexing (3/3) -- Problem Set 6 -- Part 3.Data science applications -- Chapter 18. Community detection as a communication problem -- Chapter 19. Community detection: ML principle -- Chapter 20. Community detection: An efficient algorithm -- Chapter 21. Community detection: Python implementation -- Problem Set 7 -- Chapter 22.Haplotype phasing as a communication problem -- Chapter 23. Haplotype phasing: ML principle -- Chapter 24: Haplotype phasing: An efficient algorithm. . |
| Record Nr. | UNISA-996546820903316 |
Suh Changho
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Communication Principles for Data Science / / by Changho Suh
| Communication Principles for Data Science / / by Changho Suh |
| Autore | Suh Changho |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (294 pages) |
| Disciplina | 381 |
| Collana | Signals and Communication Technology |
| Soggetto topico |
Artificial intelligence - Data processing
Digital media Computer science - Mathematics Mathematical statistics Signal processing Data Science Digital and New Media Probability and Statistics in Computer Science Signal, Speech and Image Processing Ciències de la informació Mitjans de comunicació digitals Estadística matemàtica Processament de senyals |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9789811980084
981198008X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Acknowledgements -- Part 1. Communication over the Gaussian channel -- Chapter 1.Overview of the book -- Chapter 2. A statistical model for additive noise channels -- Chapter 3. Additive Gaussian noise model -- Problem Set 1 -- Chapter 4. Optimal receiver: maximum A Posteriori (MAP) principle -- Chapter 5. Analysis of error probability -- Chapter 6. Multiple bits transmission via pulse amplitude modulation -- Problem Set 2 -- Chapter 7. Multi-shot communication -- Chapter 8. Repetition coding -- Chapter 9: Capacity of the additive white Gaussian noise channel -- Problem Set 3 -- Part 2. Communication over inter-symbol interference (ISI) channels -- Chapter 10. Signal conversion from discrete to continuous time (1/2) -- Chapter 11. Signal conversion from discrete to continuous time (2/2) -- Chapter 12. Optimal receiver architecture -- Problem Set 4 -- Chapter 13. Optimal receiver in ISI channels: maximum likelihood (ML) sequence detection -- Chapter 14. Optimal receiver in ISI channels: Viterbi algorithm -- Problem Set 5 -- Chapter 15.Orthogonal frequency division multiplexing (1/3) -- Chapter 16. Orthogonal frequency division multiplexing (2/3) -- Chapter 17. Orthogonal frequency division multiplexing (3/3) -- Problem Set 6 -- Part 3.Data science applications -- Chapter 18. Community detection as a communication problem -- Chapter 19. Community detection: ML principle -- Chapter 20. Community detection: An efficient algorithm -- Chapter 21. Community detection: Python implementation -- Problem Set 7 -- Chapter 22.Haplotype phasing as a communication problem -- Chapter 23. Haplotype phasing: ML principle -- Chapter 24: Haplotype phasing: An efficient algorithm. . |
| Record Nr. | UNINA-9910731488503321 |
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Suh Changho
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Convex Optimization for Machine Learning
| Convex Optimization for Machine Learning |
| Autore | Suh Changho |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Norwell, MA : , : Now Publishers, , 2022 |
| Descrizione fisica | 1 electronic resource (379 p.) |
| Disciplina | 006.31 |
| Collana | NowOpen |
| Soggetto topico | Optimization |
| Soggetto non controllato | Convex Optimization, Deep Learning, Generative Adversarial Networks (GANs), TensorFlow, Supervised Learning, Wasserstein GAN, Strong Duality, Weak Duality, Computed Tomography |
| ISBN | 1-63828-053-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637694403321 |
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Suh Changho
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| Norwell, MA : , : Now Publishers, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Probability for Information Technology / / by Changho Suh
| Probability for Information Technology / / by Changho Suh |
| Autore | Suh Changho |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (359 pages) |
| Disciplina | 004.015192 |
| Soggetto topico |
Computer science - Mathematics
Mathematical statistics Digital media Artificial intelligence - Data processing Machine learning Probability and Statistics in Computer Science Digital and New Media Data Science Machine Learning |
| ISBN |
9789819740321
9789819740314 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Acknowledgements -- Part I. Basic concepts of probability -- Chapter 1. Overview of the book -- Chapter 2. Sample space and events -- Chapter 3. Monty Hall problem and Python implementation -- Problem Set 1 -- Chapter 4. Conditional probability and total probability law -- Chapter 5. Independence -- Chapter 6. Coupon collector problem and Python implementation -- Problem Set 2 -- Chapter 7. Random variables -- Chapter 8. Expectation -- Chapter 9. BitTorrent and Python implementation -- Chapter 10.Variance and Chebyshev’s inequality -- Problem Set 3 -- Chapter 11.Continuous random variables -- Chapter 12. Gaussian random variables -- Problem Set 4 -- Part II. Introductory random processes and key principles -- Chapter 13. Introduction to random processes -- Chapter 14. Maximum A Posteriori (MAP) principle -- Chapter 15. MAP: Multiple observations -- Chapter 16. MAP: Performance analysis -- Chapter 17. MAP: Cancer prediciton and Python implementation -- Problem Set 5 -- Chapter 18. Maximum Likelihood Estimation (MLE) -- Chapter 19. MLE: Law of large numbers -- Chapter 20. MLE: Gaussian distribution -- Chapter 21. MLE: Gaussian distribution estimation and Python implementation -- Chapter 22. Central limit theorem -- Problem Set 6 -- Part III. Information Technology Applications -- Chapter 23. Communication: Probabilistic modeling -- Chapter 24. Communication: MAP principle -- Chapter 25. Communication: MAP under multiple observations -- Chapter 26. Communication: Repetition coding and Python implementation -- Problem Set 7 -- Chapter 27. Social networks: Probabilistic modeling -- Chapter 28. Social networks: ML principle -- Chapter 29. Social networks: Community detecition and Python implementation -- Problem Set 8 -- Chapter 30. Speech recognition: Probabilistic modeling -- Chapter 31. Speech recognition: MAP principle -- Chapter 32. Speech recognition: Viterbi algorithm -- Chapter 33. Speech recognition: Python implementation -- Problem Set 9 -- Appendix A: Python basics -- Bibliography -- Index. |
| Record Nr. | UNINA-9910983060503321 |
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Suh Changho
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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