This is a collection of papers from a large international conference on advances in machine learning and data analysis that was held at the University of California, Berkeley, in October 2008. The papers cover a range of topics related to applying machine learning techniques to systems, and data analysis and processing. Readers who work with digital systems and have some machine learning background would benefit most from this book.

There are 16 papers, organized into 16 chapters. The topics are somewhat independent but related, and most of them are in the area of electrical engineering, with machine learning or data analysis applications. The chapters cover subjects ranging from image classification to financial prediction models. Many of the research projects reported use some type of machine learning or data analysis tool. All chapters follow a standard format: a chapter title, followed by an abstract, a collection of keywords, and a group of consecutively numbered sections. Each chapter has an introductory section, a conclusion, and a bibliography that helps readers find further references, when needed.

The chapters can be divided roughly into three groups: the first applies machine learning techniques to digital systems, in order to enhance the capability or performance of the system; the second uses various data analysis techniques to accomplish a specific task; and the third involves machine learning in various other areas, such as financial optimization and student learning performance assessment. Chapters 1 to 5, 9, and 10 belong to the first group. The systems described in these chapters apply machine learning techniques to image classification, robot competence development, a watermark-based security system for media broadcasting, processor throughput prediction, continuous microarray gene expression time series, a low-cost sail simulator with automatic error correction, and a pattern classifier with feature space reduction. The second group includes chapters 7, 8, and 11 to 15. It deals with data analysis and processing in digital systems, without the explicit application of machine learning. The topics covered are: the design of a robust multi-loop proportional-integral (PI) controller for multi-time delay processes, automatic or semi-automatic detection of quasars in sky surveys, image processing, optimum use of capacitors in harmonic filters, digital pen-and-paper technology, and network-based secure assessment in classroom teaching. The last group involves two interesting subjects--one deals with a financial optimization problem and the other with the issue of student perception of course grades in self-assessment.

To conclude, the topics covered in this book should be of great interest to researchers and practitioners who want to apply machine learning technology and data analysis tools to problems in general electrical engineering areas, such as digital systems, image processing, and data collection and analysis.