• Corpus ID: 17793133

Machine learning - a probabilistic perspective

  title={Machine learning - a probabilistic perspective},
  author={Kevin P. Murphy},
  booktitle={Adaptive computation and machine learning series},
  • K. Murphy
  • Published in
    Adaptive computation and…
    24 August 2012
  • Computer Science
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as… 

Understanding Machine Learning - From Theory to Algorithms

The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way in an advanced undergraduate or beginning graduate course.

Probabilistic Data Analysis with Probabilistic Programming

Composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques, are introduced.

Introduction to Statistical Machine Learning

This introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice.

Machine Learning Background

This chapter provides an introduction to standard machine learning approaches that learn from tabular data representations, followed by an outline of approaches using various other data types

Probabilistic Data Analysis with Probabilistic Programming by Feras

This thesis introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques.

General Purpose Probabilistic Programming Platform with Effective Stochastic Inference

This work formulated structure discovery as a form of “probabilistic program synthesis”, and showed that 10 lines of code are sufficient to extend ABCD into a nonparametric Bayesian clustering technique that identifies time series which share covariance structure.

Automating inference, learning, and design using probabilistic programming

The aim of this paper is to propose a novel approach toference called Automated Variational Inference for Probabilistic Programming, which allows programmers to specify a stochastic process using syntax that resembles modern programming lan 2.

Learning Probabilistic Logic Programs in Continuous Domains

The first steps towards inducing probabilistic logic programs for continuous and mixed discrete-continuous data, without being pigeon-holed to a fixed set of distribution families are taken.

Online machine learning for combinatorial data

An online sequential action selection algorithm for the limited feedback setting (bandit feedback) and side information is developed with a linear programming relaxation of the classic maximal flow problem and an online algorithm is built that outperforms the traditional approaches under similar assumptions.

Machine learning methods for generating high dimensional discrete datasets

Two possible approaches to generating datasets that reflect patterns of real ones using a two‐step approach are explored: Constraint‐based generation and probabilistic generative modeling.



Gaussian Processes for Machine Learning

The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.

Probabilistic Graphical Models - Principles and Techniques

The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.

Computer Vision: Models, Learning, and Inference

This modern treatment of computer vision shows how to use training data to learn the relationships between the observed image data and the aspects of the world that the authors wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data.

Learning Determinantal Point Processes

This thesis shows how determinantal point processes can be used as probabilistic models for binary structured problems characterized by global, negative interactions, and demonstrates experimentally that the techniques introduced allow DPPs to be used for real-world tasks like document summarization, multiple human pose estimation, search diversification, and the threading of large document collections.

Inducing Features of Random Fields

The random field models and techniques introduced in this paper differ from those common to much of the computer vision literature in that the underlying random fields are non-Markovian and have a large number of parameters that must be estimated.

Probabilistic models of vision and max-margin methods

This paper shows that by placing bounds on the normalization constant the authors can obtain computationally tractable approximations to probabilistic methods including multi-class max- margin, ordinal regression, max-margin Markov networks and parsers, multiple-instance learning, and latent SVM.

Efficiently Inducing Features of Conditional Random Fields

This paper presents an efficient feature induction method for CRFs founded on the principle of iteratively constructing feature conjunctions that would significantly increase conditional log-likelihood if added to the model.

Large-scale kernel machines

This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets, and offers information that can address the relative lack of theoretical grounding for many useful algorithms.

Max-Margin Markov Networks

Maximum margin Markov (M3) networks incorporate both kernels, which efficiently deal with high-dimensional features, and the ability to capture correlations in structured data, and a new theoretical bound for generalization in structured domains is provided.