Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics

Max A. Little

Informasi Umum

Kode

20.21.1318

Klasifikasi

006.31 - Machine Learning

Jenis

Buku - Elektronik (E-Book)

Subjek

Machine Learning

No. Rak

Tel-U Bandung - Gedung Manterawu Lantai 5 : Rak 4
Tel-U Purwokerto : Rak 3

Dilihat

176 kali

Informasi Lainnya

Abstraksi

Digital signal processing (DSP) is one of the ‘foundational’ but somewhat invisible, engineering topics of the modern world, without which, many of the technologies we take for granted: the digital telephone, digital radio, television, CD and MP3 players, WiFi, radar, to name just a few, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies that are by now starting to reach a level of ubiquity, 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 has origins in the recent merging of classical probability and statistics with artificial intelligence, which 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 DSP and statistical machine learning make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Therefore, intimate formal links between the two subjects exist and because of this, an emerging consensus view is that DSP and statistical machine learning should not be seen as separate subjects.