TY  - GEN
N2  - This book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.
DO  - 10.1007/978-3-031-34167-0
DO  - doi
AB  - This book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.
T1  - Machine learning for astrophysics :proceedings of the ML4Astro International Conference 30 May-1 June 2022 /
AU  - Bufano, Filomena,
AU  - Riggi, Simone,
AU  - Sciacca, Eva,
AU  - Schilliro, Francesco,
VL  - volume 60
CN  - QB51.3.E43
N1  - Includes index.
ID  - 1482612
KW  - Astrophysique
KW  - Apprentissage automatique
KW  - Astrophysics
KW  - Machine learning
SN  - 9783031341670
SN  - 3031341678
TI  - Machine learning for astrophysics :proceedings of the ML4Astro International Conference 30 May-1 June 2022 /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-34167-0
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-34167-0
ER  -