File Name: information theory and statistical learning .zip
The course covers advanced methods of statistical learning. The fundamentals of Machine Learning as presented in the course "Introduction to Machine Learning" and "Advanced Machine Learning" are expanded and, in particular, the following topics are discussed:. The web-page code is based with modifications on the one of the course on Machine Learning Fall Semester ; Prof.
Information Theory and Statistical Learning presents theoretical and practical results about information theoretic methods used in the context of statistical learning. The book will present a comprehensive overview of the large range of different methods that have been developed in a multitude of contexts. Each chapter is written by an expert in the field. The book is intended for an interdisciplinary readership working in machine learning, applied statistics, artificial intelligence, biostatistics, computational biology, bioinformatics, web mining or related disciplines. I am enthusiastic about recommending the present book to researchers and students, because it summarizes most of these new emerging subjects and methods, which are otherwise scattered in many places.
Information theory, machine learning and artificial intelligence have been overlapping fields during their whole existence as academic disciplines. These areas, in turn, overlap significantly with applied and theoretical statistics. This course will explore how information-theoretic methods can be used to predict and bound the performance in statistical decision theory and in the process of learning an algorithm from data. The goal is to give PhD students in decision and control, learning, AI, network science, and information theory a solid introduction on how information-theoretic concepts and tools can be applied to problems in statistics, decision and learning well beyond their more traditional use in communication theory. Choose semester and course offering to see information from the correct course syllabus and course offering. Lecture 1: Information theory fundamentals: Entropy, mutual information, relative entropy, and f-divergence. Total variation and other distance metrics.
You'll want two copies of this astonishing book, one for the office and one for the fireside at home. NEW for teachers: all the figures available for download as well as the whole book. David J. In this book will be published by CUP. It will continue to be available from this website for on-screen viewing. Notes : Version 6.
Information theory is the scientific study of the quantification , storage , and communication of information. The field was fundamentally established by the works of Harry Nyquist and Ralph Hartley , in the s, and Claude Shannon in the s. The field is at the intersection of probability theory , statistics , computer science, statistical mechanics , information engineering , and electrical engineering. A key measure in information theory is entropy. Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process.
Machine learning relies heavily on entropy-based e. For instance, Parzen-kernel windows may be used for estimation of various probability density functions, which facilitates the expression of information theoretic concepts as kernel matrices or statistics, e. The parallels between machine learning and information theory allows the interpretation and understand of computational methods from one field in terms of their dual representations in the other. Machine learning ML is the process of data-driven estimation quantitative evidence-based learning of optimal parameters of a model, network or system, that lead to output prediction, classification, regression or forecasting based on a specific input prospective, validation or testing data, which may or may not be related to the original training data. Parameter optimality is tracked and assessed iteratively by a learning criterion depending on the specific type of ML problem. Higher-order learning criteria enable solving problems where sensitivity to higher-moments is important e. This Figure provides a schematic workflow description of machine learning.
It seems that you're in Germany. We have a dedicated site for Germany. Information Theory and Statistical Learning presents theoretical and practical results about information theoretic methods used in the context of statistical learning. The book will present a comprehensive overview of the large range of different methods that have been developed in a multitude of contexts.
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