100+ Items that are about the Concept Machine learning
Context
Context of Machine learningSubject of
 A comparative study of costsensitive boosting algorithms
 Academic Press library in signal processing, Volume 1, Signal processing theory and machine learning
 Accommodating noise during induction by generalization
 Adaptive blind signal and image processing : learning algorithms and applications
 Advanced machine learning
 Advanced machine learning with Python : solve challenging data science problems by mastering cuttingedge machine learning techniques in Python
 Advanced machine learning with scikitlearn : tools and techniques for predictive analytics in Python
 Advances in Bayesian networks
 Advances in computational intelligence and learning : methods and applications
 Advances in independent component analysis and learning machines
 Advances in large margin classifiers
 Advances in large margin classifiers
 Advances in machine learning I : dedicated to the memory of Professor Ryszard S. Michalski
 Advances in machine learning II : dedicated to the memory of Professor Ryszard S. Michalski
 Advances in machine learning applications in software engineering
 Advances in machine learning research
 Advances in minimum description length : theory and applications
 Advances in minimum description length : theory and applications
 Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimization
 Algorithmic learning
 Algorithms for reinforcement learning
 An empirical study of encoding schemes and search strategies in discovering causal networks
 An empirical study of metacost using boosting algorithms
 An experimental disproof of Occam's razor
 An improved approach for the discovery of causal models via MML
 An inductive logic programming approach to statistical relational learning
 An intelligence in our image : the risks of bias and errors in artificial intelligence
 An introduction to Support Vector Machines : and other kernelbased learning methods
 An introduction to computational learning theory
 Analysis and design of machine learning techniques : evolutionary solutions for regression, prediction, and control problems
 Apache Mahout Cookbook
 Apache Mahout clustering designs : explore the clustering algorithms used with Apache Mahout
 Apache Mahout essentials : implement topnotch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout
 Apache Spark machine learning blueprints : develop a range of cuttingedge machine learning projects with Apache Spark using this actionable guide
 Applications of learning & planning methods
 Applications of learning classifier systems
 Approaches to probabilistic model learning for mobile manipulation robots
 Approximation methods for efficient learning of Bayesian networks
 Artificial intelligence and intelligent systems : the implications
 Artificial neural networks : learning algorithms, performance evaluation, and applications
 Bayesian artificial intelligence
 Bayesian networks and BayesiaLab : a practical introduction for researchers
 Bayesian networks and decision graphs
 Bayesian networks and decision graphs
 Bayesian reasoning and machine learning
 Big data analysis : new algorithms for a new society
 Bioinformatics : the machine learning approach
 Bioinformatics : the machine learning approach
 Biomedical image analysis and machine learning technologies : applications and techniques
 Bridging the gap between graph edit distance and kernel machines
 Bridging the gap between graph edit distance and kernel machines
 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 machine learning systems with Python
 Building machine learning systems with Python : master the art of machine learning with Python and build effective machine learning sytems with this intensive handson guide
 Building machine learning systems with TensorFlow
 Building practical recommendation engines, Part 1
 Building practical recommendation engines, Part 2
 Building recommendation engines : understand your data and user preferences to make intelligent, accurate, and profitable decisions
 C4.5 : programs for machine learning
 Causal discovery with JMMLCI
 Chemoinformatics and advanced machine learning perspectives : complex computational methods and collaborative techniques
 Classifying unseen cases with many missing values
 Clojure for data science : statistics, big data, and machine learning for Clojure programmers
 Combinatorial machine learning : a rough set approach
 Compressing probabilities in Bayesian networks
 Computation and intelligence : collected readings
 Computational modeling and simulation of intellect : current state and future perspectives
 Computational trust models and machine learning
 Computer systems that learn : classification and prediction methods from statistics, neural nets, machine learning, and expert systems
 Computer vision and machine learning with RGBD sensors
 Concept data analysis : theory and applications
 Concept data analysis : theory and applications
 Concept formation : knowledge and experience in unsupervised learning
 Conformal prediction for reliable machine learning : theory, adaptations and applications
 Control, capabilities and communication : three key issues for machineexpert collaborative knowledgeacquisition
 Crossdisciplinary perspectives on the algorithm selection problem : recent advances and emerging opportunities
 DLGref2 : techniques for inductive knowledge refinement
 Data Mining and Machine Learning in Building Energy Analysis
 Data mining and machine learning in building energy analysis
 Data mining and machine learning in cybersecurity
 Data mining with decision trees : theory and applications
 Data mining with decision trees : theory and applications
 Data science : mindset, methodologies, and misconceptions
 Data science and artificial intelligence
 Data science and machine learning with PythonHands on!
 Data science with Microsoft Azure and R
 Datadriven inductive knowledgebase refinement
 Dataset shift in machine learning
 Deep learning
 Deep learning : moving toward artificial intelligence with neural networks and machine learning
 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 for strategic decision makers : understanding deep learning and how it produces business value
 Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow
 Deep learning with Python
 Deep learning with R
 Deep learning with TensorFlow
 Deep learning with TensorFlow
 Deep learning with TensorFlow : take your machine learning knowledge to the next level with the power of TensorFlow
 Deep learning with Theano : build the artificial brain of the future, today
 Demanddriven associative classification
 Design and analysis of learning classifier systems : a probabilistic approach
 Detection and identification of rare audiovisual cues
 Determine the optimal parameter for information bottleneck method
 Effective Amazon machine learning : machine learning in the Cloud
 Efficient dimensionality reduction and oneclass classification for contentbased image retrieval
 Efficient learning machines : theories, concepts, and applications for engineers and system Designers
 Elements of machine learning
 Emerging paradigms in machine learning
 Empirical attribute space refinement in classification learning
 Encoding, ensemble and accelerating strategies for linear causal model discovery
 Ensemble machine learning : methods and applications
 Evaluating Learning Algorithms : a classification perspective
 Evolutionary structure learning algorithm for Bayesian network and penalized mutual information metric
 Evolvable machines : theory & practice
 Evolving fuzzy systems : : methodologies, advanced concepts and applications
 F# for machine learning essentials : get up and running with machine learning with F# in a fun and functional way
 Foundations of learning classifier systems
 Foundations of machine learning
 Foundations of rule learning
 From curve fitting to machine learning : an illustrative guide to scientific data analysis and computational intelligence
 Fusion methods for unsupervised learning ensembles
 Fuzzy rulebased expert systems and genetic machine learning
 Generalising metalearning concepts for algorithm selection : from machine learning to optimization
 Generality is more significant than complexity : toward an alternative to Occam's razor
 Genetic algorithms for machine learning
 Genetic algorithms in search, optimization, and machine learning
 Getting started with Java deep learning
 Getting started with machine learning with R
 Getting started with tensorflow : get up and running with the latest numerical computing library by Google and dive deeper into your data!
 Handbook of learning and approximate dynamic programming
 Handson data science and Python machine learning : perform data mining and machine learning efficiently using Python and Spark
 Handson deep learning with TensorFlow : uncover what is underneath your data!
 Handson machine learning with ScikitLearn and TensorFlow : concepts, tools, and techniques to build intelligent systems
 Hardcore data science : California 2015
 Hardcore data science : NYC 2014
 Heavy lifting using R libraries
 How machines think : a general introduction to artificial intelligence ; illustrated in prolog
 How to build a person : a prolegomenon
 Human activity recognition and prediction
 Human computation
 Human recognition in unconstrained environments : using computer vision, pattern recognition and machine learning methods for biometrics
 Hybrid classifiers : methods of data, knowledge, and classifier combination
 Immunological bioinformatics
 Improving the performance of boosting for naive Bayesian classification
 Inclusive pruning : a new class of pruning axiom for unordered search and its application to classification learning
 Induction, algorithmic learning theory, and philosophy
 Inductive generalisation of complete and consistent production rules
 Information theoretic learning : Renyi's entropy and kernel perspectives
 Innovations in Bayesian networks : theory and applications
 Innovations in machine learning : theory and applications
 Instruction selection : principles, methods, and applications
 Intelligence emerging : adaptivity and search in evolving neural systems
 Intelligent data analysis for reallife applications : theory and practice
 Interdisciplinary approaches to robot learning
 Interdisciplinary approaches to robot learning
 Intrinsically motivated learning in natural and artificial systems
 Introducing data science : big data, machine learning, and more, using Python tools
 Introduction : mining the tar sands of big data
 Introduction of intelligent machine fault diagnosis and prognosis
 Introduction to Amazon machine learning : learn how to build data driven predictive applications with Amazon Web Services (AWS)
 Introduction to Apache Spark 2.0 : a primer on Spark 2.0 fundamentals and architecture
 Introduction to Pandas for developers : understand the basic workflows and gotchas of crawling, munging and plotting data
 Introduction to TensorFlowSlim : complex TensorFlow model building and training made easy
 Introduction to cognitive computing with IBM Watson Services : break free from the myths surrounding IBM Watson to learn what it really can and can't do
 Introduction to computer vision with TensorFlow : using convolutional neural networks and TensorFlow to solve computer vision tasks
 Introduction to deep learning models with TensorFlow : learn how to work with TensorFlow to create and run a TensorFlow graph, and build a deep learning model
 Introduction to machine learning
 Introduction to machine learning
 Introduction to machine learning and bioinformatics
 Introduction to machine learning and its application in chemistry
 Introduction to machine learning with Python : a guide for data scientists
 Introduction to pattern recognition and machine learning
 Is Occam's razor disposable? : in support of learning biases based on semantics rather than syntax
 Iterative sequential information bottleneck algorithm
 Java : data science made easy : data collection, processing, analysis, and more : a course in two modules
 Java data science cookbook : explore the power of MLlib, DL4j, Weka and more
 Java data science solutions : analyzing data
 Java data science solutions : big data and visualization
 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
 Julia solutions
 Kernel Methods for Pattern Analysis
 Kernel based algorithms for mining huge data sets : supervised, semisupervised, and unsupervised learning
 Kernel learning algorithms for face recognition
 Kernel methods for pattern analysis
 Kernels for structured data
 Knowledge discovery with support vector machines
 Knowledge seekerontology modelling for information search and management : a compendium
 Knowledge transfer between computer vision and text mining : similaritybased learning approaches
 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 largescale predictive applications
 Largescale kernel machines
 Latest advances in inductive logic programming
 Lazy Bayesian rules
 Lazy Bayesian rules : a lazy seminaive Bayesian learning technique competitive to boosting decision trees
 Lazy Bayesian trees
 Learn how to build intelligent data applications with Amazon Web Services (AWS) : understanding and using AWS products and services, AWS Data Pipeline, Kinesis Analytics, RDS and Redshift databases, and Amazon Machine Learning
 Learning Apache Mahout : acquire practical skills in Big Data Analytics and explore data science with Apache Mahout
 Learning Apache Mahout classification : build and personalize your own classifiers using Apache Mahout
 Learning Apache Spark 2 : process big data with the speed of light!
 Learning Bayesian models with R : become an expert in Bayesian machine learning methods using R and apply them to solve realworld big data problems
 Learning Bayesian networks
 Learning Microsoft Cognitive Services : create intelligent apps with vision, speech, language, and knowledge capabilities using Microsoft Cognitive Services
 Learning OWL class expressions
 Learning Salesforce Einstein : artificial intelligence and deep learning for your Salesforce CRM
 Learning Spark : lightening fast data analysis
 Learning algorithms : theory and applications in signal processing, control, and communications
 Learning and generalisation : with applications to neural networks
 Learning computer vision with TensorFlow
 Learning decision lists by prepending inferred rules
 Learning disjunctive characteristic descriptions by least generalization
 Learning from data : concepts, theory, and methods
 Learning from data streams in dynamic environments
 Learning in nonstationary environments : methods and applications
 Learning kernel classifiers : theory and algorithms
 Learning of fuzzy classification rules by the inexact field learning approach
 Learning path : OpenCV : realtime computer vision with OpenCV
 Learning path : Python : machine and deep learning with Python
 Learning path : R : complete machine learning and deep learning solutions
 Learning path : R programming
 Learning path : TensorFlow : the road to TensorFlow
 Learning path : TensorFlowSlim for complex model building and training
 Learning path : build your own recommendation engines
 Learning path : deep dive into Python machine learning
 Learning path : expert Python projects
 Learning path : machine learning
 Learning quantitative finance with R : implement machine learning, timeseries analysis, algorithmic trading and more
 Learning scikitlearn : machine learning in Python : experience the benefits of machine learning techniques by applying them to realworld problems using Python and the open source scikitlearn library
 Learning sites : social and technological resources for learning
 Learning to learn
 Learning with kernels : support vector machines, regularization, optimization, and beyond
 Learning, networks and statistics
 Least squares support vector machines
 Least squares support vector machines
 Logical and relational learning
 Machine Learning
 Machine Learning
 Machine Learning : an algorithmic perspective
 Machine intelligence and signal processing
 Machine interpretation of patterns : image analysis and data mining
 Machine interpretation of patterns : image analysis and data mining
 Machine learning : Apache Storm : learn by example
 Machine learning : Python programming : from beginner to intermediate
 Machine learning : Twitter sentiment analysis in Python
 Machine learning : a Bayesian and optimization perspective
 Machine learning : a multistrategy approach, Volume IV
 Machine learning : a probabilistic perspective
 Machine learning : an algorithmic perspective
 Machine learning : concepts, methodologies, tools and applications
 Machine learning : decision trees and random forests
 Machine learning : deep learning and computer vision : an introduction
 Machine learning : handson for developers and technical professionals
 Machine learning : linear and logistic regression
 Machine learning : modeling data locally and globally
 Machine learning : neural networks, genetic algorithms, and fuzzy systems
 Machine learning : quant trading
 Machine learning : recommendation systems in Python
 Machine learning : the new AI
 Machine learning : theory and applications
 Machine learning algorithms : reference guide for popular algorithms for data science and machine learning
 Machine learning algorithms for problem solving in computational applications : intelligent techniques
 Machine learning and data mining : methods and applications
 Machine learning and data mining for computer security : methods and applications
 Machine learning and data science : an introduction to statistical learning methods with R
 Machine learning and robot perception
 Machine learning and statistical modeling approaches to image retrieval
 Machine learning applications in software engineering
 Machine learning applications in software engineering
 Machine learning approaches to bioinformatics
 Machine learning for OpenCV : a practical introduction to the world of machine learning and image processing using OpenCV and Python
 Machine learning for audio, image and video analysis : theory and applications
 Machine learning for designers
 Machine learning for designers : an introduction to the core technologies of machine learning and the emerging opportunities for MLenhanced design
 Machine learning for dummies
 Machine learning for evolution strategies
 Machine learning for financial engineering
 Machine learning for human motion analysis : theory and practice
 Machine learning for multimedia content analysis
 Machine learning for protein subcellular localization prediction
 Machine learning for the web : explore the web and make smarter predictions using Python
 Machine learning for visionbased motion analysis : theory and techniques
 Machine learning forensics for law enforcement, security, and intelligence
 Machine learning in Python : essential techniques for predictive analysis
 Machine learning in bioinformatics
 Machine learning in bioinformatics
 Machine learning in complex networks
 Machine learning in computer vision
 Machine learning in computeraided diagnosis : medical imaging intelligence and analysis
 Machine learning in cyber trust : security, privacy, and reliability
 Machine learning in document analysis and recognition
 Machine learning in healthcare informatics
 Machine learning in image steganalysis
 Machine learning in medicine, Part two
 Machine learning in medicine Cookbook two
 Machine learning in medicinecookbook
 Machine learning in nonstationary environments : introduction to covariate shift adaptation
 Machine learning methods for commonsense reasoning processes : interactive models
 Machine learning of design concepts
 Machine learning of natural language
 Machine learning primer
 Machine learning projects for .NET developers
 Machine learning techniques for adaptive multimedia retrieval : technologies, applications, and perspectives
 Machine learning techniques for gait biometric recognition : using the ground reaction force
 Machine learning techniques for multimedia : case studies on organization and retrieval
 Machine learning using Python
 Machine learning using advanced algorithms and visualization in R
 Machine learning with SVM and othe kernel methods
 Machine learning with Scala
 Machine learning with Spark : create scalable machine learning applications to power a modern datadriven business using Spark
 Machine learning with Spark : develop intelligent machine learning systems with Spark 2.x
 Machine learning with TensorFlow
 Machine learning, neural and statistical classification
 Markov logic : an interface layer for artificial intelligence
 Mastering .NET machine learning : master the art of machine learning with .NET and gain insight into realworld applications
 Mastering Java machine learning : mastering and implementing advanced techniques in machine learning
 Mastering R programming
 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 : master the techniques and sophisticated analytics used to construct Sparkbased solutions that scale to deliver productiongrade data science products
 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 datadriven business using Spark
 Mastering machine learning with scikitlearn : apply effective learning algorithms to realworld problems using scikitlearn
 Mastering machine learning with scikitlearn : learn to implement and evaluate machine learning solutions with scikitlearn
 Medical image recognition, segmentation and parsing : machine learning and multiple object approaches
 Megainduction : machine learning on very large databases
 Metalearning : applications to data mining
 Microarray classification using statistical learning and decision tree algorithms
 Microsoft Azure machine learning : explore predictive analytics using stepbystep tutorials and build models to make prediction in a jiffy with a few mouse clicks
 Minimum error entropy classification
 Mining software specifications : methodologies and applications
 Modelling changes in understanding : case studies in physical reasoning
 Modelling changes in understanding : case studies in physical reasoning
 Modern machine learning techniques and their applications in cartoon animation research
 MultiObjective machine learning
 Multiagent machine learning : a reinforcement approach
 Multiboosting : a technique for combining boosting and wagging
 Multidimensional particle swarm optimization for machine learning and pattern recognition
 Multimedia database retrieval : a humancentered approach
 Multiple instance learning : foundations and algorithms
 NLTK essentials : build cool NLP and machine learning applications using NLTK and other Python libraries
 Natural computing in computational finance
 Neugierige Strukturvorschläge im maschinellen Lernen : eine technikphilosophische Verortung
 Neural networks and computing : learning algorithms and applications
 Nextgeneration sequencing and sequence data analysis
 Numerical algorithms : methods for computer vision, machine learning, and graphics
 OPUS : a systematic search algorithm and its application to categorical attributevalue datadriven machine learning
 Online sentiment : machine learning and prediction
 Ontology learning and population : bridging the gap between text and knowledge
 Optimal learning
 Parameter learning of Bayesian network by hybrid computational intelligence approach
 Particle swarm optimizer : economic dispatch with valve point effect using various PSO techniques
 Pattern classification using ensemble methods
 Pattern classification using ensemble methods
 Pattern recognition and machine learning
 Pattern recognition and machine learning
 Perceptrons : an introduction to computational geometry
 Perceptrons : an introduction to computational geometry
 Perspectives of neuralsymbolic integration
 Practical OpenCV 3 image processing with Python
 Practical machine learning : tackle the realworld complexities of modern machine learning with innovative and cuttingedge techniques
 Practical machine learning cookbook : resolving and offering solutions to your machine learning problems with R
 Practical machine learning techniques for building intelligent applications
 Practical machine learning with H2O : powerful, scalable techniques for deep learning and AI
 Predicting structured data
 Predicting structured data
 Prediction, learning, and games
 Predictive modeling of student competency
 Preference learning
 Principles and theory for data mining and machine learning
 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 : next generation techniques to revolutionize computer vision, AI, speech and data analysis
 Python machine learning : machine learning and deep learning with Python, scikitlearn, and TensorFlow
 Python machine learning : unlock deeper insights into machine learning with this vital guide to cuttingedge 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 : easytofollow 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
 Python machine learning projects
 Python machine learning solutions
 Python machine learning, Part I
 Python natural language processing : explore NLP with machine learning and deep learning techniques
 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 data analysis solutions : machine learning techniques
 R deep learning cooking : solve complex neural net problems with TensorFlow, H2O and MXNet
 R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated realworld problems successfully
 R machine learning essentials : gain quick access to the machine learning concepts and practical applications using the R development environment
 R machine learning solution
 Readings in knowledge acquisition and learning : automating the construction and improvement of expert systems
 Readings in machine learning
 Realworld machine learning
 Recognizing behavior with big data + gamification
 Reinforcement and systemic machine learning for decision making
 Robot learning
 Robust recognition via information theoretic learning
 Rough sets and data mining : analysis for imprecise data
 Rule based systems for big data : a machine learning approach
 Rule extraction from support vector machines
 Rulebased evolutionary online learning systems : a principled approach to LCS analysis and design
 Sample efficient multiagent learning in the presence of Markovian agents
 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 cuttingedge machine learning : a course in three modules
 Scala for machine learning : leverage Scala and machine learning to construct and study systems that can learn from data
 Scalable fuzzy algorithms for data management and analysis : methods and design
 Scalable machine learning : complex data analysis at scale
 Scaling data analysis with Apache Mahout
 Scientific data mining and knowledge discovery : principles and foundations
 Scikitlearn Cookbook : over 50 recipes to incorporate scikitlearn into every step of the data science pipeline, from feature extraction to model building and model evaluation
 Search strategies for induction by generalization
 Search techniques for induction by generalisation
 Secure because math? : challenges on applying machine learning to security
 Selfevolvable systems : machine learning in social media
 Semantic labeling of places with mobile robots
 Sequential methods in pattern recognition and machine learning
 Sequential methods in pattern recognition and machine learning
 Shape understanding system  Knowledge implementation and learning
 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
 Statistics for machine learning : build supervised, unsupervised, and reinforcement learning models using both Python and R
 Support vector machine in chemistry
 Support vector machines
 Support vector machines for antenna array processing and electromagnetics
 Support vector machines for pattern classification
 Support vector machines for pattern classification
 Support vector machines: theory and applications
 Systematic search for categorical attributevalue datadriven machine learning
 Teaching learning based optimization algorithm : and its engineering applications
 Temporal data mining via unsupervised ensemble learning
 Tensor voting : a perceptual organization approach to computer vision and machine learning
 TensorFlow machine learning cookbook : explore machine learning concepts using the latest numerical computing library, TensorFlow, with the help of this comprehenisive cookbook
 Testdriven machine learning : control your machine learning algorithms using testdriven development to achieve quantifiable milestones
 The Artificial intelligence debate : false starts, real foundations
 The business of deep learning : understanding deep learning and discovering real world applications
 The deep learning video collection, 2016
 The least generalization algorithm
 The road to Tensorflow
 Theory optimization : a search heuristic for datadriven machine learning
 Thoughtful machine learning with Python : a testdriven approach
 Topics in grammatical inference
 Training, evaluating, and tuning deep neural network models with TensorFlowSlim : advanced topics in training, evaluating, and tuning deep neural network models
 Truth from trash : how learning makes sense
 Tuning metaheuristics : a machine learning perspective
 Understanding complexity by clustering data with maching learning and R
 Understanding convolutional neural networks (CNNs) : learn how to implement CNNs to generate visualizations
 Understanding support vector machines
 Unsupervised discretization algorithm based on mixture probabilistic models
 Unsupervised learning algorithms
 Unsupervised learning with R : work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data
 Unsupervised process monitoring and fault diagnosis with machine learning methods
 Up and running with deep learning
 Using AI to transform search
 Visual analysis of humans : looking at people
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 faexternallinksquare fafw"></i> Data from <span resource="http://link.library.deakin.edu.au/resource/suIc0NgHKcU/" typeof="Intangible http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.deakin.edu.au/resource/suIc0NgHKcU/">Machine learning</a></span>  <span property="offers" typeOf="Offer"><span property="offeredBy" typeof="Library ll:Library" resource="http://link.library.deakin.edu.au/#Deakin%20University%20Library"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.deakin.edu.au/">Deakin University Library</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 faexternallinksquare fafw"></i> Data from <span resource="http://link.library.deakin.edu.au/resource/suIc0NgHKcU/" typeof="Intangible http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.deakin.edu.au/resource/suIc0NgHKcU/">Machine learning</a></span>  <span property="offers" typeOf="Offer"><span property="offeredBy" typeof="Library ll:Library" resource="http://link.library.deakin.edu.au/#Deakin%20University%20Library"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.deakin.edu.au/">Deakin University Library</a></span></span></span></span></div>
Structured data from the Bibframe namespace is licensed under the
Creative
Commons Attribution 4.0 International License by
Deakin University Library.
Additional terms may apply to data associated with third party namespaces.
Machine learning
 Local Identifier
 http://link.library.deakin.edu.au/resource/suIc0NgHKcU/
 Network Identifier
 http://library.link/resource/suIc0NgHKcU/
Resource Description Namespaces
 http://bibfra.me/vocab/lite/
 http://bibfra.me/vocab/marc/
 http://bibfra.me/vocab/marcext/
 http://bibfra.me/vocab/relation/
 http://creativecommons.org/ns#
 http://library.link/vocab/
 http://ogp.me/
 http://purl.org/dc/terms/
 http://schema.org/
 http://www.w3.org/1999/02/22rdfsyntaxns#
 http://www.w3.org/2000/01/rdfschema#
Network Analysis
 Inbound Links

458 1 Total458 In Library based on 1 Link Type
 Outbound Links

195 2 Total1 To Library based on 1 Link Type194 To Network based on 1 Link Type
 Shared Resources

129 1 Total129 Inbound and 1 Outbound
Shared in Network  194 Libraries
As of 20171212 at 19:25
 http://albioncollege.library.link/resource/suIc0NgHKcU
 http://almacollege.library.link/resource/suIc0NgHKcU
 http://alma.library.link/resource/suIc0NgHKcU
 http://andrews.library.link/resource/suIc0NgHKcU
 http://aadl.library.link/resource/suIc0NgHKcU
 http://aquinas.library.link/resource/suIc0NgHKcU
 http://link.arapahoelibraries.org/resource/suIc0NgHKcU
 http://link.afpls.org/resource/suIc0NgHKcU
 http://link.au.selco.info/resource/suIc0NgHKcU
 http://link.library.austintexas.gov/resource/suIc0NgHKcU
 http://baldwin.library.link/resource/suIc0NgHKcU
 http://link.balibrary.org/resource/suIc0NgHKcU
 http://baycounty.library.link/resource/suIc0NgHKcU
 http://link.bed.ent.sirsi.net/resource/suIc0NgHKcU
 http://bellaire.library.link/resource/suIc0NgHKcU
 http://link.littletongov.org/resource/suIc0NgHKcU
 http://link.law.upenn.edu/resource/suIc0NgHKcU
 http://bloomfield.library.link/resource/suIc0NgHKcU
 http://link.mybpl.org/resource/suIc0NgHKcU
 http://link.bu.edu/resource/suIc0NgHKcU
 http://bridgewater.library.link/resource/suIc0NgHKcU
 http://briggs.library.link/resource/suIc0NgHKcU
 http://link.lib.byu.edu/resource/suIc0NgHKcU
 http://link.burlingtonpl.org/resource/suIc0NgHKcU
 http://link.calgarylibrary.ca/resource/suIc0NgHKcU
 http://calvincollege.library.link/resource/suIc0NgHKcU
 http://cadl.library.link/resource/suIc0NgHKcU
 http://cass.library.link/resource/suIc0NgHKcU
 http://link.crlibrary.org/resource/suIc0NgHKcU
 http://cmich.library.link/resource/suIc0NgHKcU
 http://link.centralotagoqueenstown.kotui.org.nz/resource/suIc0NgHKcU
 http://link.ccrls.org/resource/suIc0NgHKcU
 http://cherokee.library.link/resource/suIc0NgHKcU
 http://link.goldcoast.qld.gov.au/resource/suIc0NgHKcU
 http://link.westminster.gov.uk/resource/suIc0NgHKcU
 http://clarkston.library.link/resource/suIc0NgHKcU
 http://link.cpl.org/resource/suIc0NgHKcU
 http://link.clevnet.org/resource/suIc0NgHKcU
 http://link.cphlibrary.org/resource/suIc0NgHKcU
 http://ccs.library.link/resource/suIc0NgHKcU
 http://link.coqlibrary.ca/resource/suIc0NgHKcU
 http://link.cuyahogalibrary.org/resource/suIc0NgHKcU
 http://link.dallaslibrary.org/resource/suIc0NgHKcU
 http://link.library.deakin.edu.au/resource/suIc0NgHKcU
 http://dearbornheights.library.link/resource/suIc0NgHKcU
 http://dearborn.library.link/resource/suIc0NgHKcU
 http://deltacollege.library.link/resource/suIc0NgHKcU
 http://link.denverlibrary.org/resource/suIc0NgHKcU
 http://dexter.library.link/resource/suIc0NgHKcU
 http://link.dclibrary.org/resource/suIc0NgHKcU
 http://link.dcl.org/resource/suIc0NgHKcU
 http://localhost:8000/resource/suIc0NgHKcU
 http://link.dunedin.kotui.org.nz/resource/suIc0NgHKcU
 http://link.ebrpl.com/resource/suIc0NgHKcU
 http://emich.library.link/resource/suIc0NgHKcU
 http://eastlansing.library.link/resource/suIc0NgHKcU
 http://eastpointe.library.link/resource/suIc0NgHKcU
 http://link.epl.ca/resource/suIc0NgHKcU
 http://link.ptn.lib.tx.us/resource/suIc0NgHKcU
 http://mylselibrary.library.link/resource/suIc0NgHKcU
 http://engineerradcc.library.link/resource/suIc0NgHKcU
 http://link.library.eui.eu/resource/suIc0NgHKcU
 http://link.evpl.org/resource/suIc0NgHKcU
 http://ferndale.library.link/resource/suIc0NgHKcU
 http://ferris.library.link/resource/suIc0NgHKcU
 http://finlandia.library.link/resource/suIc0NgHKcU
 http://fultonmiddlehighschool.library.link/resource/suIc0NgHKcU
 http://link.gclibrary.com/resource/suIc0NgHKcU
 http://link.gpld.org/resource/suIc0NgHKcU
 http://grandrapidscc.library.link/resource/suIc0NgHKcU
 http://grandrapids.library.link/resource/suIc0NgHKcU
 http://gvsu.library.link/resource/suIc0NgHKcU
 http://gwinnettpl.library.link/resource/suIc0NgHKcU
 http://link.hpl.ca/resource/suIc0NgHKcU
 http://link.hcpl.net/resource/suIc0NgHKcU
 http://link.hastings.kotui.org.nz/resource/suIc0NgHKcU
 http://henryford.library.link/resource/suIc0NgHKcU
 http://herrick.library.link/resource/suIc0NgHKcU
 http://hillsdalecollege.library.link/resource/suIc0NgHKcU
 http://hopecollege.library.link/resource/suIc0NgHKcU
 http://link.houstonlibrary.org/resource/suIc0NgHKcU
 http://hume.library.link/resource/suIc0NgHKcU
 http://link.library.in.gov/resource/suIc0NgHKcU
 http://jacksonville.library.link/resource/suIc0NgHKcU
 http://link.jericholibrary.org/resource/suIc0NgHKcU
 http://jessaminecpl.library.link/resource/suIc0NgHKcU
 http://joliet.library.link/resource/suIc0NgHKcU
 http://link.westfordlibrary.org/resource/suIc0NgHKcU
 http://kalamazoocollege.library.link/resource/suIc0NgHKcU
 http://kalamazoo.library.link/resource/suIc0NgHKcU
 http://link.kawerau.kotui.org.nz/resource/suIc0NgHKcU
 http://kellogcc.library.link/resource/suIc0NgHKcU
 http://kettering.library.link/resource/suIc0NgHKcU
 http://lssu.library.link/resource/suIc0NgHKcU
 http://lansingcc.library.link/resource/suIc0NgHKcU
 http://link.lvccld.org/resource/suIc0NgHKcU
 http://link.lplks.org/resource/suIc0NgHKcU
 http://link.lawrencefreelibrary.org/resource/suIc0NgHKcU
 http://ltu.library.link/resource/suIc0NgHKcU
 http://loc.library.link/resource/suIc0NgHKcU
 http://libraryofmichigan.library.link/resource/suIc0NgHKcU
 http://livonia.library.link/resource/suIc0NgHKcU
 http://mackinaw.library.link/resource/suIc0NgHKcU
 http://macombcc.library.link/resource/suIc0NgHKcU
 http://link.magiclibraries.info/resource/suIc0NgHKcU
 http://link.feilding.kotui.org.nz/resource/suIc0NgHKcU
 http://link.markhampubliclibrary.ca/resource/suIc0NgHKcU
 http://link.massey.ac.nz/resource/suIc0NgHKcU
 http://link.rochsma.selco.info/resource/suIc0NgHKcU
 http://link.mcl.org/resource/suIc0NgHKcU
 http://link.mvlc.org/resource/suIc0NgHKcU
 http://msu.library.link/resource/suIc0NgHKcU
 http://mtu.library.link/resource/suIc0NgHKcU
 http://link.midyork.org/resource/suIc0NgHKcU
 http://monroe.library.link/resource/suIc0NgHKcU
 http://link.multcolib.org/resource/suIc0NgHKcU
 http://link.napalibrary.org/resource/suIc0NgHKcU
 http://link.napervillelib.org/resource/suIc0NgHKcU
 http://link.nelson.kotui.org.nz/resource/suIc0NgHKcU
 http://niles.library.link/resource/suIc0NgHKcU
 http://northeastgeorgia.library.link/resource/suIc0NgHKcU
 http://nmu.library.link/resource/suIc0NgHKcU
 http://link.nor.selco.info/resource/suIc0NgHKcU
 http://nmc.library.link/resource/suIc0NgHKcU
 http://oaklanduniversity.library.link/resource/suIc0NgHKcU
 http://link.orl.bc.ca/resource/suIc0NgHKcU
 http://link.ocln.org/resource/suIc0NgHKcU
 http://olc.library.link/resource/suIc0NgHKcU
 http://link.palmerstonnorth.kotui.org.nz/resource/suIc0NgHKcU
 http://ppltx.library.link/resource/suIc0NgHKcU
 http://pioneerls.library.link/resource/suIc0NgHKcU
 http://plymouth.library.link/resource/suIc0NgHKcU
 http://link.lowelllibrary.org/resource/suIc0NgHKcU
 http://portage.library.link/resource/suIc0NgHKcU
 http://link.pap.lib.tx.us/resource/suIc0NgHKcU
 http://link.poklib.org/resource/suIc0NgHKcU
 http://link.livebrary.com/resource/suIc0NgHKcU
 http://saginaw.library.link/resource/suIc0NgHKcU
 http://link.rcls.org/resource/suIc0NgHKcU
 http://link.randwick.nsw.gov.au/resource/suIc0NgHKcU
 http://link.lib.rpi.edu/resource/suIc0NgHKcU
 http://rochesterhills.library.link/resource/suIc0NgHKcU
 http://link.jervislibrary.org/resource/suIc0NgHKcU
 http://link.rotorua.kotui.org.nz/resource/suIc0NgHKcU
 http://royaloak.library.link/resource/suIc0NgHKcU
 http://link.saclibrary.org/resource/suIc0NgHKcU
 http://svsu.library.link/resource/suIc0NgHKcU
 http://link.sailsinc.org/resource/suIc0NgHKcU
 http://salidis.library.link/resource/suIc0NgHKcU
 http://link.slcolibrary.org/resource/suIc0NgHKcU
 http://link.sfpl.org/resource/suIc0NgHKcU
 http://link.smcl.org/resource/suIc0NgHKcU
 http://link.sccl.org/resource/suIc0NgHKcU
 http://link.sspl.org/resource/suIc0NgHKcU
 http://link.selwyn.kotui.org.nz/resource/suIc0NgHKcU
 http://sequoyah.library.link/resource/suIc0NgHKcU
 http://link.snoisle.org/resource/suIc0NgHKcU
 http://springarbor.library.link/resource/suIc0NgHKcU
 http://springlake.library.link/resource/suIc0NgHKcU
 http://link.stcharleslibrary.org/resource/suIc0NgHKcU
 http://stclair.library.link/resource/suIc0NgHKcU
 http://stclairshores.library.link/resource/suIc0NgHKcU
 http://sterling.library.link/resource/suIc0NgHKcU
 http://swan.library.link/resource/suIc0NgHKcU
 http://link.tasman.kotui.org.nz/resource/suIc0NgHKcU
 http://thomastownship.library.link/resource/suIc0NgHKcU
 http://troy.library.link/resource/suIc0NgHKcU
 http://link.tulsalibrary.org/resource/suIc0NgHKcU
 http://link.tynglib.org/resource/suIc0NgHKcU
 http://unitysd.library.link/resource/suIc0NgHKcU
 http://udmercy.library.link/resource/suIc0NgHKcU
 http://link.liverpool.ac.uk/resource/suIc0NgHKcU
 http://link.lib.umanitoba.ca/resource/suIc0NgHKcU
 http://umichdearborn.library.link/resource/suIc0NgHKcU
 http://link.library.missouri.edu/resource/suIc0NgHKcU
 http://link.libraries.ou.edu/resource/suIc0NgHKcU
 http://vanburen.library.link/resource/suIc0NgHKcU
 http://link.vpl.ca/resource/suIc0NgHKcU
 http://link.vaughanpl.info/resource/suIc0NgHKcU
 http://link.vestalpubliclibrary.org/resource/suIc0NgHKcU
 http://link.waimakariri.kotui.org.nz/resource/suIc0NgHKcU
 http://link.wccls.org/resource/suIc0NgHKcU
 http://link.library.waubonsee.edu/resource/suIc0NgHKcU
 http://waynestate.library.link/resource/suIc0NgHKcU
 http://westbloomfield.library.link/resource/suIc0NgHKcU
 http://link.westchesterlibraries.org/resource/suIc0NgHKcU
 http://wmich.library.link/resource/suIc0NgHKcU
 http://link.wpcl.lib.pa.us/resource/suIc0NgHKcU
 http://link.westlakelibrary.org/resource/suIc0NgHKcU
 http://westland.library.link/resource/suIc0NgHKcU
 http://link.wfpl.net/resource/suIc0NgHKcU
 http://link.worthingtonlibraries.org/resource/suIc0NgHKcU
 http://yavapailn.library.link/resource/suIc0NgHKcU
 http://link.yumalibrary.org/resource/suIc0NgHKcU
Processing Feedback ...