#
Machine learning
Resource Information
The concept ** Machine learning** represents the subject, aboutness, idea or notion of resources found in **University of Missouri Libraries**.

The Resource
Machine learning
Resource Information

The concept

**Machine learning**represents the subject, aboutness, idea or notion of resources found in**University of Missouri Libraries**.- Label
- Machine learning

## Context

Context of Machine learning#### Subject of

- 3D hand pose estimation via a lightweight deep learning model
- A Data Science Approach to Extracting Insights About Cities and Zones Using Open Government Data
- A deep learning method for protein model quality assessment
- A learning receiver for communication in three-component multipath channels
- A machine learning approach to phishing detection and defense
- A machine-learning decision-support tool for travel-demand modeling
- Adaptive blind signal and image processing : learning algorithms and applications
- Adaptive temporal difference learning of spatial memory in the water maze task
- Advanced data analytics using Python : with machine learning, deep learning and NLP examples
- Advanced machine learning with Python : solve challenging data science problems by mastering cutting-edge machine learning techniques in Python
- Advanced structured prediction
- Advances in financial machine learning
- Algorithmic learning
- Algorithms for reinforcement learning
- Algorithms for reinforcement learning
- An introduction to Bayesian networks
- An introduction to machine learning interpretability : an applied perspective on fairness, accountability, transparency, and explainable AI
- Analysis of flame images in gas-fired furnaces
- Apache Mahout Cookbook
- Apache Mahout clustering designs : explore the clustering algorithms used with Apache Mahout
- Apache Mahout essentials : implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout
- Apache Spark machine learning blueprints : develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide
- Architecting system of systems: artificial life analysis of financial market behavior
- Artificial intelligence and intelligent systems : the implications
- Artificial intelligence and machine learning in libraries
- Artificial neural networks : learning algorithms, performance evaluation, and applications
- Automated prediction of hepatic arterial stenosis
- Automatic learning techniques in power systems
- Autonomous learning systems : from data streams to knowledge in real-time
- Bayesian learning
- Bayesian learning for neural networks
- Bayesian networks and decision graphs
- Best practices for bringing AI to the enterprise
- Big data analysis : new algorithms for a new society
- Bioinformatics : the machine learning approach
- Bioinformatics : the machine learning approach
- Building Cognitive Applications with IBM Watson Services : Volume 2 Conversation
- Building Machine Learning Systems with Python
- Building a recommendation engine with Scala : learn to use Scala to build a recommendation engine from scratch and empower your website users
- Building a recommendation system with R : learn the art of building robust and powerful recommendation engines using R
- Building machine learning projects with TensorFlow : engaging projects that will teach you how complex data can be exploited to gain the most insight
- Building recommendation engines : understand your data and user preferences to make intelligent, accurate, and profitable decisions
- C4.5 : programs for machine learning
- CLASSMATE : Computerized Learning Agent Solving Simultaneous MAThematical Equations
- Classification of human postural and gestural movements using center of pressure parameters derived from force platforms
- Clojure for data science : statistics, big data, and machine learning for Clojure programmers
- Clustering : Methodology, hybrid systems, visualization, validation and implementation
- Cognitive carpentry : a blueprint for how to build a person
- Computational intelligence in business analytics : concepts, methods, and tools for big data applications
- Computational learning theory : an introduction
- Computational methods of feature selection
- Computer vision and machine learning with RGB-D sensors
- Concept data analysis : theory and applications
- Concurrent learning and information processing : a neuro-computing system that learns during monitoring, forecasting, and control
- Conformal prediction for reliable machine learning : theory, adaptations, and applications
- Connectionist models and their implications : readings from cognitive science
- DL-DI : a deep learning framework for distributed, incremental image classification
- DMLA : a dynamic model-based Lambda Architecture for learning and recognition of features in Big Data
- Dan Van boxel's deep learning with TensorFlow
- Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification
- Data science : mindset, methodologies, and misconceptions
- Data science algorithms in a week : data analysis, machine learning, and more
- Data science and machine learning with Python - hands on!
- Deep Learning with TensorFlow
- Deep Learning with Theano
- Deep learning
- Deep learning : a practitioner's approach
- Deep learning : practical neural networks with Java : build and run intelligent applications by leveraging key Java machine learning libraries : a course in three modules
- Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow
- Deep learning with PyTorch : a practical approach to building neural network models using PyTorch
- Discrete sequence prediction and its applications
- Dynamic model generation and semantic search for open source projects using big data analytics
- Effective Amazon Machine Learning
- Efficient learning machines : theories, concepts, and applications for engineers and system Designers
- Evaluating Learning Algorithms : A Classification Perspective
- Exemplar-based knowledge acquisition : a unified approach to concept representation, classification, and learning
- F# for machine learning essentials : get up and running with machine learning with F# in a fun and functional way
- Fast learning and invariant object recognition : the sixth generation breakthrough
- Feature discovery through error-correction learning
- Feature engineering made easy : identify unique features from your dataset in order to build powerful machine learning systems
- Feature-based analysis for open source using big data analytics
- Foundations of inductive logic programming
- Foundations of inductive logic programming
- Foundations of machine learning
- Fundamentals of deep learning : designing next-generation machine intelligence algorithms
- Fuzzy adaptive resonance theory: Applications and extensions
- Fuzzy mathematical approach to pattern recognition
- Fuzzy rule-based expert systems and genetic machine learning
- Genetic algorithms in search, optimization, and machine learning
- Genetic learning for adaptive image segmentation
- Getting started with TensorFlow
- Getting started with deep learning
- Getting started with tensorflow : get up and running with the latest numerical computing library by Google and dive deeper into your data!
- Graph-theoretic techniques for web content mining
- Hands-On Data Science and Python Machine Learning
- Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine
- Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems
- Hello, TensorFlow! : building and training your first TensorFlow graph from the ground up
- How to build a person : a prolegomenon
- Human recognition in unconstrained environments : using computer vision, pattern recognition and machine learning methods for biometrics
- Hydrogeochemical controls on reactive and nonreactive solute transport in heterogenous porous media
- Improving performance through concept formation and conceptual clustering
- Individual differences in hemispheric specialization
- Induction : processes of inference, learning, and discovery
- Inductive learning algorithms for complex systems modeling
- Inductive logic programming
- Information theoretic learning : Renyi's entropy and kernel perspectives
- Intelligence : the eye, the brain, and the computer
- Interactive theory revision : an inductive logic programming approach
- Into the heart of the mind : an American quest for artificial intelligence
- Introducing data science : big data, machine learning, and more, using Python tools
- Introduction to deep learning business applications for developers : from conversational bots in customer service to medical image processing
- Introduction to machine learning
- Introduction to machine learning
- Introduction to machine learning with Python : a guide for data scientists
- Introduction to pattern recognition and machine learning
- Introduction to semi-supervised learning
- Introduction to semi-supervised learning
- Introduction to semi-supervised learning
- Java : data science made easy : data collection, processing, analysis, and more : a course in two modules
- Java deep learning essentials : dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java
- Java for data science : examine the techniques and Java tools supporting the growing field of data science
- KB4DL : building a knowledge base for deep learning
- KVM Virtualization Cookbook
- Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning
- Kernel methods for pattern analysis
- Knowledge acquisition and machine learning : theory, methods, and applications
- Knowledge discovery with support vector machines
- LESLIE : LEarning System for simultaneous LInear Equations
- Large scale machine learning with Spark : discover everything you need to build robust machine learning applications with Spark 2.0
- Large scale machine learning with python : learn to build powerful machine learning models quickly and deploy large-scale predictive applications
- Learning Apache Mahout : acquire practical skills in Big Data Analytics and explore data science with Apache Mahout
- Learning Bayesian models with R : become an expert in Bayesian machine learning methods using R and apply them to solve real-world big data problems
- Learning Bayesian networks
- Learning Salesforce Einstein
- Learning Spark : lightening fast data analysis
- Learning TensorFlow : a guide to building deep learning systems
- Learning algorithms : theory and applications in signal processing, control, and communications
- Learning and coordination : enhancing agent performance through distributed decision making
- Learning and inference in computational systems biology
- Learning apache mahout classification : build and personalize your own classifiers using apache mahout
- Learning automata : theory and applications
- Learning automata and stochastic optimization
- Learning board game strategies by modifying evaluation functions
- Learning by experimentation
- Learning classifier systems : from foundations to applications
- Learning classifier systems : from foundations to applications
- Learning in humans and machines : towards an interdisciplinary learning science
- Learning quantitative finance with R : implement machine learning, time-series analysis, algorithmic trading and more
- Learning scikit-learn : machine learning in Python : experience the benefits of machine learning techniques by applying them to real-world problems using Python and the open source scikit-learn library
- Learning sites : social and technological resources for learning
- Learning to classify text using support vector machines
- Learning to learn
- Learning, networks and statistics
- Logic for learning : learning comprehensible theories from structured data
- Logical and relational learning
- MATLAB deep learning : with machine learning, neural networks and artificial intelligence
- Machine Learning
- Machine intelligence : Turing and after
- Machine learning : a Bayesian and optimization perspective
- Machine learning : a Bayesian and optimization perspective
- Machine learning : a Bayesian and optimization perspective
- Machine learning : a guide to current research
- Machine learning : a probabilistic perspective
- Machine learning : a probabilistic perspective
- Machine learning : algorithms and applications
- Machine learning : an artificial intelligence approach
- Machine learning : hands-on for developers and technical professionals
- Machine learning : hands-on for developers and technical professionals
- Machine learning : modeling data locally and globally
- Machine learning : theory and applications
- Machine learning and data mining : methods and applications
- Machine learning and data mining in pattern recognition : 7th International Conference, MLDM 2011, New York, NY, USA, August/September 2011, proceedings
- Machine learning and data science : an introduction to statistical learning methods with R
- Machine learning and image interpretation
- Machine learning and its applications : advanced lectures
- Machine learning and its applications : advanced lectures
- Machine learning and knowledge acquisition : integrated approaches
- Machine learning and knowledge discovery for engineering systems health management
- Machine learning and statistical modeling approaches to image retrieval
- Machine learning approaches to bioinformatics
- Machine learning for audio, image and video analysis : theory and applications
- Machine learning for designers
- Machine learning for dummies
- Machine learning for email
- Machine learning for financial engineering
- Machine learning for the web : explore the web and make smarter predictions using Python
- Machine learning in action
- Machine learning in bioinformatics
- Machine learning in computer vision
- Machine learning in image steganalysis
- Machine learning in medicine
- Machine learning in non-stationary environments : introduction to covariate shift adaptation
- Machine learning methods for ecological applications
- Machine learning methods in the environmental sciences : neural networks and kernels
- Machine learning of design concepts
- Machine learning of inductive bias
- Machine learning of natural language
- Machine learning projects for .NET Developers
- Machine learning using R
- Machine learning with Go : implement regression, classification, clustering, time-series models, neural networks, and more using the Go programming language
- Machine learning with Spark : create scalable machine learning applications to power a modern data-driven business using Spark
- Machine learning with Swift : artificial intelligence for iOS
- Machine learning with TensorFlow 1.x : second generation machine learning with Google's brainchild - TensorFlow 1.x
- Machines that learn : based on the principles of empirical control
- Mahout in action
- Markov logic : an interface layer for artificial intelligence
- Mastering .NET machine learning : master the art of machine learning with .NET and gain insight into real-world applications
- Mastering Java Machine Learning
- Mastering Scala machine learning : advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop
- Mastering Spark for data science
- Mastering TensorFlow 1.x : advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
- Mastering machine learning with R : advanced prediction, algorithms, and learning methods with R 3.x
- Mastering machine learning with R : master machine learning techniques with R to deliver insights for complex projects
- Mastering machine learning with Spark 2.x : create scalable machine learning applications to power a modern data-driven business using Spark
- Mastering predictive analytics with R : machine learning techniques for advanced models
- Memory-based learning structure : learning covergence [sic], network structures and training techniques
- Metalearning : applications to data mining
- Metric learning
- Microsoft Azure machine learning : explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks
- Mind bugs : the origins of procedural misconceptions
- Modelling changes in understanding : case studies in physical reasoning
- Multi-agent systems : integrating reinforcement learning, bidding and genetic algorithms
- Multidimensional particle swarm optimization for machine learning and pattern recognition
- Multiple Instance Learning : Foundations and Algorithms
- Myths and legends in learning classification rules
- NLTK essentials : build cool NLP and machine learning applications using NLTK and other Python libraries
- Natural computing in computational finance
- Natural language annotation for machine learning
- Networks of learning automata : techniques for online stochastic optimization
- Neural networks and computing : learning algorithms and applications
- Neural networks and deep learning
- Neural networks and statistical learning
- New neural network structures for problems with high-dimensional input space
- Nonnegative matrix and tensor factorizations : applications to exploratory multi-way data analysis and blind source separation
- Novel approaches to clustering, biclustering algorithms based on adaptive resonance theory and intelligent control
- Object detection and classification using shape feature
- On being a machine
- Optimal learning
- Oracle business intelligence with machine learning : artificial intelligence techniques in OBIEE for actionable BI
- Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python
- Paradigms for machine learning
- Pattern classification using ensemble methods
- Pattern recognition and machine learning
- Pattern recognition and machine learning
- Pattern recognition and machine learning : proceedings
- Planning and learning by analogical reasoning
- Planning and learning by analogical reasoning
- Practical Convolutional Neural Networks : Implement advanced deep learning models using Python
- Practical artificial intelligence : machine learning, bots, and agent solutions using C#
- Practical big data analytics : hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R
- Practical machine learning : a new look at anomaly detection
- Practical machine learning : tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques
- Practical machine learning cookbook : resolving and offering solutions to your machine learning problems with R
- Practical machine learning with H2O : powerful, scalable techniques for deep learning and AI
- Practical machine learning with Python : a problem-solver's guide to building real-world intelligent systems
- Practical time-series analysis : master time series data processing, visualization, and modeling using python
- Predicting hotspots : using machine learning to understand civil conflict
- Prediction, learning, and games
- Principles and theory for data mining and machine learning
- Principles and theory for data mining and machine learning
- Pro deep learning with TensorFlow : a mathematical approach to advanced artificial intelligence in Python
- Probabilistic inductive logic programming : theory and applications
- Probability for statistics and machine learning : fundamentals and advanced topics
- Python : deeper insights into machine learning : leverage benefits of machine learning techniques using Python : a course in three modules
- Python : real world machine learning : learn to solve challenging data science problems by building powerful machine learning models using Python
- Python Deep Learning
- Python Natural Language Processing
- Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow
- Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics
- Python machine learning blueprints : intuitive data projects you can relate to : an approachable guide to applying advanced machine learning methods to everyday problems
- Python machine learning by example : easy-to-follow examples that get you up and running with machine learning
- Python machine learning cookbook : 100 recipes that teach you how to perform various machine learning tasks in the real world
- Quantum Machine Learning : What Quantum Computing Means to Data Mining
- R : recipes for analysis, visualization and machine learning : get savvy with R language and actualize projects aimed at analysis, visualization and machine learning
- R : unleash machine learning techniques : find out how to build smarter machine learning systems with R : follow this three module course to become a more fluent machine learning practitioner : a course in three modules
- R Deep learning essentials : build automatic classification and prediction models using unsupervised learning
- R deep learning cookbook : solve complex neural net problems with TensorFlow, H2O and MXNet
- R deep learning projects : master the techniques to design and develop neural network models in R
- R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully
- Real-world machine learning
- Reasoning with probabilistic and deterministic graphical models : exact algorithms
- Recurrent neural networks for prediction : learning algorithms, architectures, and stability
- Reinforcement and systemic machine learning for decision making
- Representation in incremental learning
- Robot learning
- Robot shaping : an experiment in behavior engineering
- Rough sets and data mining : analysis for imprecise date
- Scala : applied machine learning : leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scala's most advanced and finest features : a course in three modules
- Scala : guide for data science professionals : Scala will be a valuable tool to have on hand during your data science journey for everything from data cleaning to cutting-edge machine learning : a course in three modules
- Scala for machine learning : data processing, ML algorithms, smart analytics, and more
- Scala for machine learning : leverage Scala and machine learning to construct and study systems that can learn from data
- Scikit-learn : machine learning simplified
- Scikit-learn Cookbook : over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation
- Semantic labeling of places with mobile robots
- Sequence learning : paradigms, algorithms, and applications
- Sequence learning : paradigms, algorithms, and applications
- Sequential methods in pattern recognition and machine learning
- SigSpace {u2013} Class-Based Feature Representation for Scalable and Distributed Machine Learning
- Signal processing theory and machine learning
- SigsSpace-Text : parallel and distributed signature learning in text analytics
- Soft computing in machine learning
- Spark GraphX in action
- Spark for data science : analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0
- Support vector machine in chemistry
- Support vector machines
- Support vector machines for antenna array processing and electromagnetics
- Support vector machines for pattern classification
- Symbolic visual learning
- Temporal Data Mining via Unsupervised Ensemble Learning
- Tensor voting : a perceptual organization approach to computer vision and machine learning
- Tensor voting : a perceptual organization approach to computer vision and machine learning
- TensorFlow : powerful predictive analytics with TensorFlow : predict valuable insights of your data with TensorFlow
- TensorFlow Machine Learning Cookbook
- TensorFlow for dummies
- Test-Driven Machine Learning
- The BOXES methodology : black box dynamic control
- The cross-entropy method : a unified approach to combinatorial optimization, Monte-Carlo simulation, and machine learning
- The voice in the machine : building computers that understand speech
- The voice in the machine : building computers that understand speech
- Thinking between the lines : computers and the comprehension of causal descriptions
- Thinking machines : the evolution of artificial intelligence
- Thoughtful machine learning with Python : a test-driven approach
- Training neural pattern classifers with a mean field theory learning algorithm
- Understanding support vector machines
- Unsupervised learning with R : work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data
- Using machine learning to create turbine performance models
- iHear {u2013} Lightweight Machine Learning Engine with Context Aware Audio Recognition Model
- mGA1.0 : a common LISP implementation of a messy genetic algorithm

## Embed (Experimental)

### Settings

Select options that apply then copy and paste the RDF/HTML data fragment to include in your application

Embed this data in a secure (HTTPS) page:

Layout options:

Include data citation:

<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/resource/suIc0NgHKcU/" typeof="CategoryCode http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/resource/suIc0NgHKcU/">Machine learning</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>

Note: Adjust the width and height settings defined in the RDF/HTML code fragment to best match your requirements

### Preview

## Cite Data - Experimental

### Data Citation of the Concept Machine learning

Copy and paste the following RDF/HTML data fragment to cite this resource

`<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/resource/suIc0NgHKcU/" typeof="CategoryCode http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/resource/suIc0NgHKcU/">Machine learning</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>`