Microsoft Research Institute's new book "Fundamentals of data science", 479 Pages pdf

Book address ：https://doi.org/10.1017/9781108755528

This book introduces the mathematical and Algorithmic Foundations of Data Science , Including machine learning , High dimensional geometry , And analysis of large networks . Topics include the counterintuitive nature of high-dimensional data , Important linear algebra techniques , Such as singular value decomposition , Random walk and Markov chain theory , Basic principles and important algorithms of machine learning , Clustering algorithm and analysis , Probabilistic models for large networks , Representation learning includes topic modeling and nonnegative matrix decomposition 、 Wavelet and compressed sensing . Developed important probability Technology , Including the law of large numbers 、 Tail inequality 、 Random projection analysis 、 Generalization guarantee in machine learning , And the moment method used to analyze the phase transition in large random graphs . Besides , Matrix norms and vc Dimension and other important structural and complexity metrics . This book is suitable for undergraduate and graduate courses to design and analyze algorithm data .