Description
Book SynopsisDescribes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.
Trade ReviewThis book provides an excellent pathway for gaining first-class expertise in machine learning. It provides both the technical background that explains why certain approaches, but not others, are best practice in real world problems, and a framework for how to think about and approach new problems. I highly recommend it for people with a signal processing background who are seeking to become an expert in machine learning. * Alex 'Sandy' Pentland, Toshiba Professor of Media Arts and Sciences, Massachusetts Institute of Technology, *
Over the past decade in signal processing, machine learning has gone from a disparate research field known only to people working on topics such as speech and image processing, to permeating all aspects of it. With this book, Prof. Little has taken an important step in unifying machine learning and signal processing. As a whole, this book covers many topics, new and old, that are important in their own right and equips the reader with a broader perspective than traditional signal processing textbooks. In particular, I would highlight the combination of statistical modeling, convex optimization, and graphs as particularly potent. Machine learning and signal processing are no longer separate, and there is no doubt in my mind that this is the way to teach signal processing in the future. * Mads Christensen, Full Professor in Audio Processing, Aalborg University, Denmark, *
Table of Contents1: Mathematical Foundations 2: Optimization 3: Random Sampling 4: Statistical Modelling and Inference 5: Probabalistic Graphical Models 6: Statistical Machine Learning 7: Linear-Gaussian Systems and Signal Processing 8: Discrete Signals: Sampling, Quantization and Coding 9: Nonlinear and Non-Gaussian Signal Processing 10: Nonparametric Bayesian Machine Learning and Signal Processing
