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Probabilistic Machine Learning

Advanced Topics

Kevin P. Murphy

Adaptive Computation and Machine Learning series

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Résumé

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À propos de ce livre

Langue :English
ISBN :9780262376006
Date de parution :16 nov. 2022
Éditeur :MIT Press

Marque éditoriale :The MIT Press
Format de fichier :EPUB3
Taille de fichier :37.63 MB
Options de téléchargement :EPUB3 (Adobe DRM)

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Vos paramètres de confidentialité

Probabilistic Machine Learning

Résumé

À propos de ce livre

Détails du livre

Détails sur l'accessibilité

Comment lire

Dans cette série

Probabilistic Machine Learning

Machine Learning

Lectures semblables

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Introduction to Machine Learning, fourth edition

Machine Learning

Machine Learning

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Statistical Analysis Techniques in Particle Physics

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Machine Learning - A Journey To Deep Learning: With Exercises And Answers

Algorithms for Optimization

Applied Bayesian Statistics

Pathways to Machine Learning and Soft Computing

An Introduction to Machine Learning

Computational Statistics

An Elementary Introduction to Statistical Learning Theory

Bayesian Essentials with R

Algorithms for Decision Making

Probability and Statistics with Reliability, Queuing, and Computer Science Applications

Probability, Random Variables, and Random Processes

Core Concepts in Data Analysis: Summarization, Correlation and Visualization

Simulation-Based Algorithms for Markov Decision Processes

Handbook of Computational Statistics

New Handbook of Mathematical Psychology: Volume 1, Foundations and Methodology

Fuzzy Logic with Engineering Applications

Adaptive Filters

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Probability, Random Processes, and Statistical Analysis

Bayesian Filtering and Smoothing

Stochastic Processes

Local Polynomial Modelling and Its Applications

Variational Methods for Machine Learning with Applications to Deep Networks

Advanced Markov Chain Monte Carlo Methods

Algorithmic Learning Theory

Introduction to Online Convex Optimization, second edition

Chain Event Graphs

Probabilistic Machine Learning for Civil Engineers

Handbook of Mixture Analysis

Introduction to Statistical Machine Learning

Mixtures

Exploratory Data Analysis with MATLAB

Artificial Intelligence and Causal Inference

Data Science for Engineers

Artificial General Intelligence

Algorithms for Sparsity-Constrained Optimization

Practical Mathematics for AI and Deep Learning

Modern Methodology and Applications in Spatial-Temporal Modeling

The Mathematics Of Generalization

Machine Learning

An Introduction to Pattern Recognition and Machine Learning

Smoothing and Regression

Elements of Statistical Computing

Uncertainty Modeling

Statistical Inference and Machine Learning for Big Data

Decision Making Under Uncertainty and Reinforcement Learning

Learning in Non-Stationary Environments

Optimal Experimental Design

Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video

System Parameter Identification

The Design and Analysis of Computer Experiments

Computational Statistics in Data Science

Machine Learning for Speaker Recognition

Mixture and Hidden Markov Models with R

Advances in Modeling and Simulation

Quantitative Evaluation of Systems

Pattern Recognition and Classification

Design of Experiments in Nonlinear Models

Bayesian and High-Dimensional Global Optimization

Large-Scale Inverse Problems and Quantification of Uncertainty

Neural Networks: Tricks of the Trade