000			03036nam a2200337Ia 4500
000			04990nam a22003735i 4500
001			978-981-19-8008-4
003			DE-He213
005			20240319120930.0
007			cr nn 008mamaa
008			230612s2023 si | s |||| 0|eng d
020			_a9789811980084 _9978-981-19-8008-4
082			_a5.7
100			_aSuh, Changho. _932947
245			_aCommunication Principles for Data Science _cby Changho Suh. _h[electronic resource] /
250			_a1st ed. 2023.
260			_aSingapore _bSpringer Nature Singapore _c2023
300			_aXIV, 283 p. 131 illus., 103 illus. in color. _bonline resource.
520			_aThis book introduces the basic principles underlying the design and analysis of the digital communication systems that have heralded the information revolution. One major goal of the book is to demonstrate the role of the digital communication principles in a wide variety of data science applications, including community detection, computational biology, speech recognition and machine learning. One defining feature of this book is to make an explicit connection between the communication principles and data science problems, as well as to succinctly deliver the "story" of how the communication principles play a role for trending data science applications. All the key "plots" involved in the story are coherently developed with the help of tightly coupled exercise problem sets, and the associated fundamentals are explored mostly from first principles. Another key feature is that it includes programming implementation of a variety of algorithms inspired by fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python and TensorFlow. This book does not follow a traditional book-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent storylines and themes. It serves as a textbook mainly for a junior- or senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in probability and random processes, and basic familiarity with Python. But the background can be supplemented by almost self-contained materials, as well as by numerous exercise problems intended for elaborating on non-trivial concepts. In addition, Part III for data science applications should provide motivation and insights to students and even professional scientists who are interested in the field.
650			_aArtificial intelligence _932948
650			_aComputer science _932949
650			_aData Science. _932950
650			_aDigital and New Media. _932951
650			_aDigital media. _932952
650			_aMathematical statistics. _932953
650			_aProbability and Statistics in Computer Science. _932954
650			_aSignal processing. _932955
650			_aSignal, Speech and Image Processing . _932956
856			_uhttps://doi.org/10.1007/978-981-19-8008-4
942			_cEBK _2ddc
999			_c15343 _d15343