Introduction to Statistical Machine Learning
2015 оны 10-р сар · Morgan Kaufmann
Электрон ном
534
Хуудас
family_home
Боломжит
info
Энэ электрон номын тухай
Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. - Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus - Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning - Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks - Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials
Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image proces...
Бусад номтой танилцах
Зохиогчийн тухай
Takashi Ishida is a Research Scientist at RIKEN AIP and an Associate Professor at The University of Tokyo. He received his PhD from the University of Tokyo, advised by Prof. Masashi Sugiyama. Prior to that, he received the MSc from the University of Tokyo and the Bachelor of Economics from Keio University. His research interests include statistical machine learning and its applications, with a focus on model and agent evaluation, alignment, and safety.
Takashi Ishida is a Research Scientist at RIKEN AIP and an Associate Professor at The University of Tokyo. He received his PhD from the University of Tokyo, advised by Prof. Masashi Sugiyama. Prior to that, he received the MSc from the University of Tokyo and the Bachelor of Economics from Keio U...
Энэ электрон номыг үнэлэх
Санал бодлоо хэлнэ үү.
Унших мэдээлэл
Ухаалаг утас болон таблет
Андройд болон iPad/iPhone-д Google Ном Унших аппыг суулгана уу. Үүнийг таны бүртгэлд автоматаар синк хийх бөгөөд та хүссэн газраасаа онлайн эсвэл офлайнаар унших боломжтой.
Зөөврийн болон ердийн компьютер
Та компьютерийн веб хөтчөөр Google Play-с авсан аудио номыг сонсох боломжтой.
eReaders болон бусад төхөөрөмжүүд
Kobo Цахим ном уншигч гэх мэт e-ink төхөөрөмжүүд дээр уншихын тулд та файлыг татаад төхөөрөмж рүүгээ дамжуулах шаардлагатай болно. Файлуудаа дэмжигддэг Цахим ном уншигч руу шилжүүлэхийн тулд Тусламжийн төвийн дэлгэрэнгүй зааварчилгааг дагана уу.
