Machine learning for signal processing: data science, algorithms, and computational statistics
"Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learnin...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Oxford
Oxford University Press
2019
|
| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | TUM01 UPA01 Volltext |
| Zusammenfassung: | "Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic"-- |
| Beschreibung: | 1908 |
| Beschreibung: | 1 Online-Ressource (XVIII, 359 Seiten) Illustrationen, Diagramme |
| ISBN: | 9780191024313 9780191879180 |
| DOI: | 10.1093/oso/9780198714934.001.0001 |
Internformat
MARC
| LEADER | 00000nmm a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV046889108 | ||
| 003 | DE-604 | ||
| 005 | 20230524 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 200908s2019 xxk|||| o||u| ||||||eng d | ||
| 020 | |a 9780191024313 |9 978-0-19-102431-3 | ||
| 020 | |a 9780191879180 |c Online |9 978-0-19-187918-0 | ||
| 024 | 7 | |a 10.1093/oso/9780198714934.001.0001 |2 doi | |
| 035 | |a (OCoLC)1197709031 | ||
| 035 | |a (DE-599)BVBBV046889108 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a xxk |c XA-GB | ||
| 049 | |a DE-91 |a DE-739 | ||
| 084 | |a ST 620 |0 (DE-625)143684: |2 rvk | ||
| 084 | |a ZN 6040 |0 (DE-625)157496: |2 rvk | ||
| 100 | 1 | |a Little, Max A. |e Verfasser |0 (DE-588)1084128101 |4 aut | |
| 245 | 1 | 0 | |a Machine learning for signal processing |b data science, algorithms, and computational statistics |c Max A. Little |
| 250 | |a First edition | ||
| 264 | 1 | |a Oxford |b Oxford University Press |c 2019 | |
| 300 | |a 1 Online-Ressource (XVIII, 359 Seiten) |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a 1908 | ||
| 520 | 3 | |a "Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic"-- | |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Data Science |0 (DE-588)1140936166 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Signalverarbeitung |0 (DE-588)4054947-1 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Signalverarbeitung |0 (DE-588)4054947-1 |D s |
| 689 | 0 | 2 | |a Data Science |0 (DE-588)1140936166 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 978-0-19-871493-4 |
| 856 | 4 | 0 | |u https://doi.org/10.1093/oso/9780198714934.001.0001 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-4-NLEBK |a ZDB-28-OSD | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032298970 | ||
| 966 | e | |u https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=2227465&site=ehost-live |l TUM01 |p ZDB-4-NLEBK |q TUM_Einzelkauf |x Aggregator |3 Volltext | |
| 966 | e | |u https://doi.org/10.1093/oso/9780198714934.001.0001 |l UPA01 |p ZDB-28-OSD |q UPA_PDA_OSD_Kauf2021-22 |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804181753309429760 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Little, Max A. |
| author_GND | (DE-588)1084128101 |
| author_facet | Little, Max A. |
| author_role | aut |
| author_sort | Little, Max A. |
| author_variant | m a l ma mal |
| building | Verbundindex |
| bvnumber | BV046889108 |
| classification_rvk | ST 620 ZN 6040 |
| collection | ZDB-4-NLEBK ZDB-28-OSD |
| ctrlnum | (OCoLC)1197709031 (DE-599)BVBBV046889108 |
| discipline | Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| discipline_str_mv | Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| doi_str_mv | 10.1093/oso/9780198714934.001.0001 |
| edition | First edition |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03515nmm a2200493 c 4500</leader><controlfield tag="001">BV046889108</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20230524 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">200908s2019 xxk|||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780191024313</subfield><subfield code="9">978-0-19-102431-3</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780191879180</subfield><subfield code="c">Online</subfield><subfield code="9">978-0-19-187918-0</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1093/oso/9780198714934.001.0001</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1197709031</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV046889108</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">xxk</subfield><subfield code="c">XA-GB</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield><subfield code="a">DE-739</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 620</subfield><subfield code="0">(DE-625)143684:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ZN 6040</subfield><subfield code="0">(DE-625)157496:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Little, Max A.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1084128101</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine learning for signal processing</subfield><subfield code="b">data science, algorithms, and computational statistics</subfield><subfield code="c">Max A. Little</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">First edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Oxford</subfield><subfield code="b">Oxford University Press</subfield><subfield code="c">2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XVIII, 359 Seiten)</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">1908</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">"Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic"--</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Data Science</subfield><subfield code="0">(DE-588)1140936166</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Signalverarbeitung</subfield><subfield code="0">(DE-588)4054947-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Signalverarbeitung</subfield><subfield code="0">(DE-588)4054947-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Data Science</subfield><subfield code="0">(DE-588)1140936166</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-0-19-871493-4</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1093/oso/9780198714934.001.0001</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-NLEBK</subfield><subfield code="a">ZDB-28-OSD</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032298970</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=2227465&site=ehost-live</subfield><subfield code="l">TUM01</subfield><subfield code="p">ZDB-4-NLEBK</subfield><subfield code="q">TUM_Einzelkauf</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1093/oso/9780198714934.001.0001</subfield><subfield code="l">UPA01</subfield><subfield code="p">ZDB-28-OSD</subfield><subfield code="q">UPA_PDA_OSD_Kauf2021-22</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV046889108 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T15:20:33Z |
| indexdate | 2024-07-10T08:56:40Z |
| institution | BVB |
| isbn | 9780191024313 9780191879180 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032298970 |
| oclc_num | 1197709031 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-739 |
| owner_facet | DE-91 DE-BY-TUM DE-739 |
| physical | 1 Online-Ressource (XVIII, 359 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-4-NLEBK ZDB-28-OSD ZDB-4-NLEBK TUM_Einzelkauf ZDB-28-OSD UPA_PDA_OSD_Kauf2021-22 |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Oxford University Press |
| record_format | marc |
| spelling | Little, Max A. Verfasser (DE-588)1084128101 aut Machine learning for signal processing data science, algorithms, and computational statistics Max A. Little First edition Oxford Oxford University Press 2019 1 Online-Ressource (XVIII, 359 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier 1908 "Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic"-- Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Signalverarbeitung (DE-588)4054947-1 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Signalverarbeitung (DE-588)4054947-1 s Data Science (DE-588)1140936166 s DE-604 Erscheint auch als Druck-Ausgabe 978-0-19-871493-4 https://doi.org/10.1093/oso/9780198714934.001.0001 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Little, Max A. Machine learning for signal processing data science, algorithms, and computational statistics Maschinelles Lernen (DE-588)4193754-5 gnd Data Science (DE-588)1140936166 gnd Signalverarbeitung (DE-588)4054947-1 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)1140936166 (DE-588)4054947-1 |
| title | Machine learning for signal processing data science, algorithms, and computational statistics |
| title_auth | Machine learning for signal processing data science, algorithms, and computational statistics |
| title_exact_search | Machine learning for signal processing data science, algorithms, and computational statistics |
| title_exact_search_txtP | Machine learning for signal processing data science, algorithms, and computational statistics |
| title_full | Machine learning for signal processing data science, algorithms, and computational statistics Max A. Little |
| title_fullStr | Machine learning for signal processing data science, algorithms, and computational statistics Max A. Little |
| title_full_unstemmed | Machine learning for signal processing data science, algorithms, and computational statistics Max A. Little |
| title_short | Machine learning for signal processing |
| title_sort | machine learning for signal processing data science algorithms and computational statistics |
| title_sub | data science, algorithms, and computational statistics |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Data Science (DE-588)1140936166 gnd Signalverarbeitung (DE-588)4054947-1 gnd |
| topic_facet | Maschinelles Lernen Data Science Signalverarbeitung |
| url | https://doi.org/10.1093/oso/9780198714934.001.0001 |
| work_keys_str_mv | AT littlemaxa machinelearningforsignalprocessingdatasciencealgorithmsandcomputationalstatistics |