Automated data analysis techniques are necessary given the Web-enabled flood of electronic data we face today. These are provided by machine learning, which creates techniques that can automatically find patterns in data and utilize those patterns to forecast upcoming data.
Based on a unified, probabilistic approach, this textbook provides a thorough and self-contained introduction to the area of machine learning.
The coverage is both comprehensive and in-depth, including the basic knowledge of subjects like probability, optimization, and linear algebra that is important, as well as a discussion of contemporary advancements in the field such as conditional random fields, L1 regularization, and deep learning.
The book is presented in an approachable, conversational tone and includes pseudo-code for the most crucial algorithms. Numerous color visuals and actual examples from several application fields, including biology, text processing, computer vision, and robotics, are used to explain each topic.
