# Index

## A

- Abstract-C / Abstract-C
- active learning
- about / Active learning
- representation / Representation and notation
- notation / Representation and notation
- scenarios / Active learning scenarios
- approaches / Active learning approaches
- uncertainty sampling / Uncertainty sampling
- version space sampling / Version space sampling

- active learning, case study
- about / Case study in active learning
- tools / Tools and software
- software / Tools and software
- business problem / Business problem
- machine learning, mapping / Machine learning mapping
- data collection / Data Collection
- data sampling / Data sampling and transformation
- data transformation / Data sampling and transformation
- feature analysis / Feature analysis and dimensionality reduction
- dimensionality reduction / Feature analysis and dimensionality reduction
- models / Models, results, and evaluation
- results / Models, results, and evaluation
- evaluation / Models, results, and evaluation
- results, pool-based scenarios / Pool-based scenarios
- results, stream-based scenarios / Stream-based scenarios
- results, analysis / Analysis of active learning results

- ADaptable sliding WINdow (ADWIN)
- about / Sliding windows

- adaptation methods
- about / Adaptation methods
- explicit adaptation / Explicit adaptation
- implicit adaptation / Implicit adaptation

- Advanced Message Queueing Protocol (AMQP) / Message queueing frameworks
- affinity propagation
- about / Affinity propagation
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- algorithms, comparing
- McNemars Test / McNemar's Test
- Wilcoxon signed-rank test / Wilcoxon signed-rank test

- Amazon Elastic MapReduce (EMR) / Amazon Elastic MapReduce
- Amazon Kinesis / Publish-subscribe frameworks
- Amazon Redshift / Amazon Redshift
- Angle-based Outlier Degree (ABOD) / How does it work?
- anomaly detection
- about / Outlier or anomaly detection

- ANOVA test / ANOVA test
- Apache Kafka / Publish-subscribe frameworks
- Apache Storm / SAMOA as a real-time Big Data Machine Learning framework
- Approx Storm / Approx Storm
- ArangoDB / Graph databases
- association analysis / Machine learning – types and subtypes
- Autoencoders
- about / Autoencoders
- mathematical notations / Definition and mathematical notations
- loss function / Loss function
- limitations / Limitations of Autoencoders
- denoising / Denoising Autoencoder

- axioms of probability / Axioms of probability

## B

- Balanced Iterative Reducing and Clustering Hierarch (BIRCH)
- about / Hierarchical based and micro clustering
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Batch Big Data Machine Learning
- about / Batch Big Data Machine Learning
- used, as H2O / H2O as Big Data Machine Learning platform

- Bayesian information score (BIC) / Measures to evaluate structures
- Bayesian networks
- about / Bayesian networks
- representation / Bayesian networks, Representation
- inference / Bayesian networks, Inference
- learning / Bayesian networks, Learning

- Bayes theorem
- about / Bayes' theorem
- density, estimation / Density estimation
- mean / Mean
- variance / Variance
- standard deviation / Standard deviation
- Gaussian standard deviation / Gaussian standard deviation
- covariance / Covariance
- correlation coefficient / Correlation coefficient
- binomial distribution / Binomial distribution
- Poisson distribution / Poisson distribution
- Gaussian distribution / Gaussian distribution
- central limit theorem / Central limit theorem
- error propagation / Error propagation

- Bernoulli distribution / Random variables, joint, and marginal distributions
- Big Data
- characteristics / What are the characteristics of Big Data?
- volume / What are the characteristics of Big Data?
- velocity / What are the characteristics of Big Data?
- variety / What are the characteristics of Big Data?
- veracity / What are the characteristics of Big Data?

- Big Data cluster deployment frameworks
- about / Big Data cluster deployment frameworks
- Hortonworks Data Platform (HDP) / Hortonworks Data Platform
- Cloudera CDH / Cloudera CDH
- Amazon Elastic MapReduce (EMR) / Amazon Elastic MapReduce
- Microsoft Azure HDInsight / Microsoft Azure HDInsight

- Big Data framework
- about / General Big Data framework
- cluster deployment frameworks / Big Data cluster deployment frameworks
- data acquisition / Data acquisition
- data storage / Data storage
- data preparation / Data processing and preparation
- data processing / Data processing and preparation
- machine learning / Machine Learning
- visualization / Visualization and analysis
- analysis / Visualization and analysis

- Big Data Machine Learning
- about / Big Data Machine Learning
- framework / General Big Data framework
- Big Data framework / General Big Data framework
- Spark MLlib / Spark MLlib as Big Data Machine Learning platform

- binomial distribution / Binomial distribution
- boosting
- about / Boosting
- algorithm input / Algorithm inputs and outputs
- algorithm output / Algorithm inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitation / Advantages and limitations

- bootstrap aggregating (bagging)
- about / Bootstrap aggregating or bagging
- algorithm inputs / Algorithm inputs and outputs
- algorithm outputs / Algorithm inputs and outputs
- working / How does it work?
- Random Forest / Random Forest
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Broyden-Fletcher-Goldfarb-Shanno (BFGS) / How does it work?
- Business Intelligence (BI) / What is not machine learning?
- business problem / Business problem, Business problem

## C

- case study
- about / Case study
- business problem / Business problem
- machine learning mapping / Machine learning mapping
- data sampling and transformation / Data sampling and transformation
- feature analysis / Feature analysis
- Models, results, and evaluation / Models, results, and evaluation
- results, analysis / Analysis of results

- case study, with CoverType dataset
- about / Case study
- business problem / Business problem
- machine learning, mapping / Machine Learning mapping
- data collection / Data collection
- data sampling / Data sampling and transformation
- data transformation / Data sampling and transformation
- Big Data Machine Learning, used as Spark MLlib / Spark MLlib as Big Data Machine Learning platform

- Cassandra / Columnar databases
- central limit theorem / Central limit theorem
- Chi-Squared feature / Statistical approach
- Chunk / H2O architecture
- classification
- about / Formal description and notation

- Classification and Regression Trees (CART) / Decision Trees
- Clique tree or junction tree algorithm, Bayesian networks
- about / Clique tree or junction tree algorithm
- input and output / Input and output
- working / How does it work?
- advantages and limitations / Advantages and limitations

- Cloudera CDH / Cloudera CDH
- Cluster-based Local Outlier Factor (CBLOF)
- about / How does it work?

- clustering
- about / Clustering
- spectral clustering / Spectral clustering
- affinity propagation / Affinity propagation
- using, for incremental unsupervised learning / Incremental unsupervised learning using clustering
- evaluation techniques / Validation and evaluation techniques
- validation techniques / Validation and evaluation techniques
- stream cluster evaluation, key issues / Key issues in stream cluster evaluation
- evaluation measures / Evaluation measures

- clustering-based methods
- about / Clustering-based methods
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- clustering algorithms
- about / Clustering algorithms
- k-means / k-Means
- DBSCAN / DBSCAN
- mean shift / Mean shift
- Gaussian mixture modeling (GMM) / Expectation maximization (EM) or Gaussian mixture modeling (GMM)
- expectation maximization (EM) / Expectation maximization (EM) or Gaussian mixture modeling (GMM)
- hierarchical clustering / Hierarchical clustering
- self-organizing maps (SOM) / Self-organizing maps (SOM)

- clustering evaluation
- about / Clustering validation and evaluation
- internal evaluation measures / Clustering validation and evaluation, Internal evaluation measures
- external evaluation measures / Clustering validation and evaluation, External evaluation measures

- Clustering Features (CF) / Hierarchical based and micro clustering
- Clustering Feature Tree (CF Tree) / Hierarchical based and micro clustering
- clustering techniques
- about / Clustering techniques
- generative probabilistic models / Generative probabilistic models
- distance-based text clustering / Distance-based text clustering
- non-negative Matrix factorization (NMF) / Non-negative matrix factorization (NMF)

- Clustering Trees (CT) / Hierarchical based and micro clustering
- clustering validation
- about / Clustering validation and evaluation

- Cluster Mapping Measures (CMM)
- about / Cluster Mapping Measures (CMM)
- mapping phase / Cluster Mapping Measures (CMM)
- penality phase / Cluster Mapping Measures (CMM)

- cluster mode / Amazon Elastic MapReduce
- cluster SSL
- about / Cluster and label SSL
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- limitations / Advantages and limitations
- advantages / Advantages and limitations

- CluStream
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- co-training SSL
- about / Co-training SSL or multi-view SSL
- inputs / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- columnar databases / Columnar databases
- concept drift
- about / Concept drift and drift detection

- conditional probability distribution (CPD) / Factor types, Definition
- Conditional random fields (CRFs) / Conditional random fields
- confusion matrix / Confusion matrix and related metrics
- connectivity-based outliers (COF) / How does it work?
- contrastive divergence (CD) / Contrastive divergence
- Convolutional Neural Networks (CNN)
- about / Convolutional Neural Network
- local connectivity / Local connectivity
- parameter sharing / Parameter sharing
- discrete convolution / Discrete convolution
- Pooling or Subsampling / Pooling or subsampling
- ReLU / Normalization using ReLU

- coreference resolution / Coreference resolution
- correlation-based feature selection (CFS) / Correlation-based feature selection (CFS)
- Correlation based Feature selection (CFS)
- about / Feature selection

- correlation coefficient / Correlation coefficient
- Cosine distance / Cosine distance
- covariance / Covariance
- CoverType dataset
- reference link / Business problem

- Cross Industry Standard Process (CRISP)
- about / Process

- cumulative sum (CUSUM) / CUSUM and Page-Hinckley test
- custom frameworks / Custom frameworks

## D

- (DBSCAN)
- about / DBSCAN
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- D-Separation, Bayesian networks / D-Separation
- data acquisition
- about / Data acquisition
- publish-subscribe frameworks / Publish-subscribe frameworks
- source-sink frameworks / Source-sink frameworks
- SQL frameworks / SQL frameworks
- message queueing frameworks / Message queueing frameworks
- custom frameworks / Custom frameworks

- data analysis
- about / Data analysis
- label analysis / Label analysis
- features analysis / Features analysis

- data collection
- mapping / Data collection

- data distribution sampling
- about / Data distribution sampling
- working / How does it work?
- model change / Expected model change
- error reduction / Expected error reduction
- advantages / Advantages and limitations
- disadvantages / Advantages and limitations

- Data Frame / H2O architecture
- data management
- about / Data management

- data preparation
- key tasks / Data processing and preparation
- HQL / Hive and HQL
- Hive / Hive and HQL
- Spark SQL / Spark SQL
- Amazon Redshift / Amazon Redshift
- real-time stream processing / Real-time stream processing

- data preprocessing
- about / Data transformation and preprocessing

- data processing
- key tasks / Data processing and preparation
- HQL / Hive and HQL
- Hive / Hive and HQL
- Spark SQL / Spark SQL
- Amazon Redshift / Amazon Redshift
- real-time stream processing / Real-time stream processing

- data quality analysis
- about / Data quality analysis

- data sampling
- about / Data sampling, Data sampling and transformation
- need for / Is sampling needed?
- undersampling / Undersampling and oversampling
- oversampling / Undersampling and oversampling
- stratified sampling / Stratified sampling
- techniques / Training, validation, and test set
- experiments / Experiments, results, and analysis
- results / Experiments, results, and analysis
- analysis / Experiments, results, and analysis, Feature relevance and analysis
- feature relevance / Feature relevance and analysis
- test data, evaluation / Evaluation on test data
- results, analysis / Analysis of results

- datasets
- used, in machine learning / Datasets used in machine learning
- structured data / Datasets used in machine learning
- transaction data / Datasets used in machine learning
- market data / Datasets used in machine learning
- unstructured data / Datasets used in machine learning
- sequential data / Datasets used in machine learning
- graph data / Datasets used in machine learning

- datasets, machine learning
- about / Datasets
- UC Irvine (UCI) database / Datasets
- Tunedit / Datasets
- Mldata.org / Datasets
- KDD Challenge Datasets / Datasets
- Kaggle / Datasets

- data storage
- about / Data storage
- HDFS / HDFS
- NoSQL / NoSQL

- data transformation
- about / Data transformation and preprocessing, Data sampling and transformation
- feature, construction / Feature construction
- missing values, handling / Handling missing values
- outliers, handling / Outliers
- discretization / Discretization
- data sampling / Data sampling
- training / Training, validation, and test set
- validation / Training, validation, and test set
- test set / Training, validation, and test set

- Davies-Bouldin index / Davies-Bouldin index
- Silhouettes index / Silhouette's index

- Decision Trees
- about / Decision Trees
- algorithm input / Algorithm inputs and outputs
- algorithm output / Algorithm inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Deep Autoencoders
- about / Deep Autoencoders

- Deep Belief Networks (DBN)
- inputs and outputs / Inputs and outputs
- working / How does it work?

- Deep feed-forward NN
- about / Deep feed-forward NN
- input and outputs / Input and outputs
- working / How does it work?

- deep learning
- about / Deep learning
- building blocks / Building blocks for deep learning
- Rectified linear activation function / Rectified linear activation function
- Restricted Boltzmann Machines / Restricted Boltzmann Machines
- Autoencoders / Autoencoders
- Unsupervised pre-training and supervised fine-tuning / Unsupervised pre-training and supervised fine-tuning
- Deep feed-forward NN / Deep feed-forward NN , How does it work?
- Deep Autoencoders / Deep Autoencoders
- Deep Belief Networks (DBN) / Deep Belief Networks, Inputs and outputs
- Dropouts / Deep learning with dropouts, Definition and mathematical notation
- sparse coding / Sparse coding
- Convolutional Neural Network (CNN) / Convolutional Neural Network
- Convolutional Neural Network (CNN) layers / CNN Layers
- Recurrent Neural Networks (RNN) / Recurrent Neural Networks

- Deep Learning
- about / Deep learning and NLP

- Deep Learning (DL) / Feature relevance and analysis
- deep learning, case study
- about / Case study
- tools and software / Tools and software
- business problem / Business problem
- machine learning mapping / Machine learning mapping
- feature analysis / Feature analysis
- models, results and evaluation / Models, results, and evaluation
- basic data handling / Basic data handling
- multi-layer Perceptron / Multi-layer perceptron
- MLP, parameters / Multi-layer perceptron
- MLP, code for / Code for MLP
- Convolutional Network / Convolutional Network
- Convolutional Network, code for / Code for CNN
- Variational Autoencoder / Variational Autoencoder, Code for Variational deep learning, case studyVariational AutoencoderAutoencoder
- DBN / DBN
- parameter search, Arbiter used / Parameter search using Arbiter
- results and analysis / Results and analysis

- DeepLearning4J
- URL / Machine learning – tools and datasets
- about / Machine learning – tools and datasets

- denisity
- estimation / Density estimation

- density-based methods / Outliers
- about / Density-based methods
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- density based algorithm
- about / Density based
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- desccriptive quality analysis
- about / Descriptive data analysis
- basic label analysis / Basic label analysis
- basic feature analysis / Basic feature analysis

- detection methods
- model evolution, monitoring / Monitoring model evolution
- distribution changes, monitoring / Monitoring distribution changes

- Deviance-Threshold Measure / Measures to evaluate structures
- Dice coefficient / Dice coefficient
- dimensionality reduction
- about / Feature relevance analysis and dimensionality reduction, Feature analysis and dimensionality reduction
- notation / Notation
- linear models / Linear methods
- nonlinear methods / Nonlinear methods
- PCA / PCA
- random projections / Random projections
- ISOMAP / ISOMAP
- observation / Observations on feature analysis and dimensionality reduction

- Directed Acyclic Graph (DAG) / Definition
- Direct Update of Events (DUE) / Direct Update of Events (DUE)
- Dirichlet distribution / Prior and posterior using the Dirichlet distribution
- discretization
- about / Discretization
- by binning / Discretization
- by frequency / Discretization
- by entropy / Discretization

- distance-based clustering
- for outlier detection / Distance-based clustering for outlier detection

- distance-based methods / Outliers
- about / Distance-based methods
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- distribution changes, monitoring
- about / Monitoring distribution changes
- Welchs test / Welch's t test
- Kolmogorov-Smirnovs test / Kolmogorov-Smirnov's test
- Page-Hinckley test / CUSUM and Page-Hinckley test
- cumulative sum (CUSUM) / CUSUM and Page-Hinckley test

- document collection
- about / Document collection and standardization
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?

- document databases / Document databases
- document frequency (DF) / Frequency-based techniques
- drift detection
- about / Concept drift and drift detection
- data management / Data management
- partial memory / Partial memory

- drift detection method (DDM) / Drift Detection Method or DDM
- Dropouts
- about / Deep learning with dropouts
- definition and mathematical notation / Definition and mathematical notation, How does it work?
- training with / Learning Training and testing with dropouts
- testing with / Learning Training and testing with dropouts

- Dunns Indices / Dunn's Indices

## E

- early drift detection method (EDDM) / Early Drift Detection Method or EDDM
- eigendecomposition / Eigendecomposition
- ELEC dataset / Data collection
- elimination-based inference, Bayesian networks
- about / Elimination-based inference
- variable elimination algorithm / Variable elimination algorithm
- input and output / Input and output
- VE algorithm, advantages / Advantages and limitations

- Elki
- about / Machine learning – tools and datasets
- URL / Machine learning – tools and datasets

- EM (Diagonal Gaussian Model Factory) / Clustering models, results, and evaluation
- embedded approach / Embedded approach
- ensemble algorithms
- about / Ensemble algorithms
- weighted majority algorithm (WMA) / Weighted majority algorithm
- online bagging algorithm / Online Bagging algorithm
- online boosting algorithm / Online Boosting algorithm

- ensemble learning
- about / Ensemble learning and meta learners
- types / Ensemble learning and meta learners
- bootstrap aggregating (bagging) / Bootstrap aggregating or bagging
- boosting / Boosting

- error propagation / Error propagation
- error reduction
- variance reduction / Variance reduction
- density weighted methods / Density weighted methods

- Euclidean distance / Euclidean distance
- EUPLv1.1
- URL / Machine learning – tools and datasets

- evaluation criteria
- accuracy / Evaluation criteria
- balanced accuracy / Evaluation criteria
- Area under ROC curve (AUC) / Evaluation criteria
- Kappa statistic (K) / Evaluation criteria
- Kappa Plus statistic / Evaluation criteria

- evaluation measures, clustering
- Cluster Mapping Measures (CMM) / Cluster Mapping Measures (CMM)
- V-Measure / V-Measure
- other measures / Other external measures
- purity / Other external measures
- entropy / Other external measures
- Recall / Other external measures
- F-Measure / Other external measures
- Precision / Other external measures

- Exact Storm / Exact Storm
- Expectation Maximization (EM) / Advantages and limitations
- about / Expectation maximization (EM) or Gaussian mixture modeling (GMM), How does it work?, How does it work?

- extended Jaccard Coefficient / Extended Jaccard coefficient
- external evaluation measures
- about / External evaluation measures
- Rand index / Rand index
- F-Measure / F-Measure
- normalized mutual information index (NMI) / Normalized mutual information index

## F

- F-Measure / F-Measure
- False Positive Rate (FPR) / Confusion matrix and related metrics
- feature analysis
- about / Feature analysis and dimensionality reduction
- notation / Notation
- observation / Observations on feature analysis and dimensionality reduction

- feature evaluation techniques
- about / Feature evaluation techniques
- filter approach / Filter approach
- wrapper approach / Wrapper approach
- embedded approach / Embedded approach

- feature extraction/generation
- about / Feature extraction/generation
- lexical features / Lexical features
- syntactic features / Syntactic features
- semantic features / Semantic features

- feature relevance analysis
- about / Feature relevance analysis and dimensionality reduction
- feature search techniques / Feature search techniques
- feature evaluation techniques / Feature evaluation techniques

- features
- construction / Feature construction

- feature search techniques / Feature search techniques
- feature selection
- about / Feature selection
- Information theoretic techniques / Information theoretic techniques
- statistical-based techniques / Statistical-based techniques
- frequency-based techniques / Frequency-based techniques

- filter approach
- about / Filter approach
- univariate feature selection / Univariate feature selection
- multivariate feature selection / Multivariate feature selection

- Fine Needle Aspirate (FNA) / Datasets and analysis
- flow of influence, Bayesian networks / Flow of influence
- Friedmans test / Friedman's test

## G

- Gain charts / Gain charts and lift curves
- Gain Ratio (GR) / Information theoretic techniques
- Gaussian distribution / Gaussian distribution
- Gaussian Mixture Model (GMM) / Gaussian Mixture Model
- Gaussian mixture modeling (GMM)
- about / Expectation maximization (EM) or Gaussian mixture modeling (GMM), How does it work?
- input / Input and output
- output / Input and output
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Gaussian Radial Basis Kernel / How does it work?
- Gaussian standard deviation / Gaussian standard deviation
- Generalized Linear Models (GLM) / Feature relevance and analysis
- generative probabilistic models
- about / Generative probabilistic models
- input / Input and output
- output / Input and output
- working / How does it work?
- advantages / Advantages and limitations
- limitation / Advantages and limitations

- Gibbs parameterization / Gibbs parameterization
- Gini index / How does it work?
- Gradient Boosting Machine (GBM) / Feature relevance and analysis
- graph
- concepts / Graph concepts
- structure and properties / Graph structure and properties
- subgraphs and cliques / Subgraphs and cliques
- path / Path, trail, and cycles
- trail / Path, trail, and cycles
- cycles / Path, trail, and cycles

- graph data / Datasets used in machine learning
- graph databases / Graph databases
- graph mining / Machine learning – types and subtypes
- GraphX
- about / Machine learning – tools and datasets
- URL / Machine learning – tools and datasets

- grid based algorithm
- about / Grid based
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

## H

- H2O
- about / Machine learning – tools and datasets
- URL / Machine learning – tools and datasets
- as Big Data Machine Learning platform / H2O as Big Data Machine Learning platform
- architecture / H2O architecture
- machine learning / Machine learning in H2O
- tools / Tools and usage
- usage / Tools and usage

- HBase / Columnar databases
- HDFS
- about / HDFS

- HDFS, components / HDFS
- NameNode / HDFS
- Secondary NameNode / HDFS
- DataNode / HDFS

- Hidden Markov models
- about / Hidden Markov models for NER
- input / Input and output
- output / Input and output
- working / How does it work?
- advantages / Advantages and limitations
- limitation / Advantages and limitations

- Hidden Markov models (HMM) / Hidden Markov models
- hidden Markov models (HMM) / How does it work?
- hierarchical clustering
- about / Hierarchical clustering
- input / Input and output
- output / Input and output
- working / How does it work?
- single linkage / How does it work?
- complete linkage / How does it work?
- average linkage / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- high-dimensional-based methods
- about / High-dimensional-based methods
- inputs / Inputs and outputs
- ouputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Hive / Hive and HQL
- Hoeffding Trees (HT) / Hoeffding trees or very fast decision trees (VFDT)
- input / Inputs and outputs
- output / Inputs and outputs
- limitations / Advantages and limitations
- advantages / Advantages and limitations

- Horse Colic Classification, case study
- about / Case Study – Horse Colic Classification
- reference link / Case Study – Horse Colic Classification
- business problem / Business problem
- machine learning, mapping / Machine learning mapping
- data analysis / Data analysis
- supervised learning, experiments / Supervised learning experiments
- analysis / Results, observations, and analysis

- Hortonworks Data Platform (HDP) / Hortonworks Data Platform
- HQL / Hive and HQL
- Hyperbolic tangent ( / Hyperbolic tangent ("tanh") function
- hyperplane / How does it work?

## I

- I-Map, Bayesian networks / I-Map
- incremental learning / Machine learning – types and subtypes
- incremental supervised learning
- about / Incremental supervised learning
- modeling techniques / Modeling techniques
- validation / Validation, evaluation, and comparisons in online setting
- evaluation / Validation, evaluation, and comparisons in online setting
- comparisons, in online setting / Validation, evaluation, and comparisons in online setting
- model validation techniques / Model validation techniques

- incremental unsupervised learning
- clustering, using / Incremental unsupervised learning using clustering
- modeling techniques / Modeling techniques

- independent, identical distributions (i.i.d.) / Monitoring model evolution
- Independent Component Analysis (ICA) / Advantages and limitations
- inference, Bayesian networks
- about / Inference
- elimination-based inference / Elimination-based inference
- propagation-based techniques / Propagation-based techniques
- sampling-based techniques / Sampling-based techniques

- inferencing / Machine learning – types and subtypes, Semantic reasoning and inferencing
- influence space (IS) / How does it work?
- information extraction / Information extraction and named entity recognition
- Information gain (IG) / Information theoretic techniques
- internal evaluation measures
- about / Internal evaluation measures
- compactness / Internal evaluation measures
- separation / Internal evaluation measures
- notation / Notation
- R-Squared / R-Squared
- Dunns Indices / Dunn's Indices
- Davies-Bouldin index / Davies-Bouldin index

- Internet of things (IoT) / Machine learning applications
- Interquartile Ranges (IQR) / Outliers
- inverse document frequency (IDF) / Inverse document frequency (IDF)
- Isomap / Advantages and limitations
- iterative reweighted least squares (IRLS) / How does it work?

## J

- Java Class Library for Active Learning (JCLAL)
- URL / Machine learning – tools and datasets
- about / Machine learning – tools and datasets
- reference link / Tools and software

- JavaScript Object Notation (JSON) / Document collection and standardization
- JKernelMachines (Transductive SVM) / Tools and software
- joint distribution / Random variables, joint, and marginal distributions, Factor types

## K

- k-means
- about / k-Means
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- k-Nearest Neighbors (k-NN) / Outliers
- K-Nearest Neighbors (KNN)
- about / K-Nearest Neighbors (KNN)
- algorithm input / Algorithm inputs and outputs
- algorithm output / Algorithm inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Kaggle
- about / Datasets
- URL / Datasets

- KDD Challenge Datasets
- URL / Datasets
- about / Datasets

- KEEL
- about / Machine learning – tools and datasets
- URL / Machine learning – tools and datasets

- KEEL (Knowledge Extraction based on Evolutionary Learning)
- about / Tools and software
- reference link / Tools and software

- kernel density estimation (KDE) / How does it work?
- Kernel Principal Component Analysis (KPCA)
- about / Kernel Principal Component Analysis (KPCA)
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- kernel trick / How does it work?
- key-value databases / Key-value databases
- Key Performance Indicators (KPIs) / What is not machine learning?
- KNIME
- about / KNIME
- references / KNIME

- Knime
- about / Machine learning – tools and datasets
- URL / Machine learning – tools and datasets

- Kohonen networks / How does it work?
- Kolmogorov-Smirnovs test / Kolmogorov-Smirnov's test
- Kubat / Widmer and Kubat
- Kullback-Leibler (KL) / How does it work?

## L

- label SSL
- about / Cluster and label SSL
- output / Inputs and outputs
- input / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Latent Dirichlet Allocation (LDA) / Advantages and limitations
- latent semantic analysis (LSA) / Dimensionality reduction
- learning
- techniques / Training, validation, and test set

- learning, Bayesian networks
- goals / Learning
- parameters / Learning parameters
- Maximum likelihood estimation (MLE) / Maximum likelihood estimation for Bayesian networks
- Bayesian parameter, estimation / Bayesian parameter estimation for Bayesian network
- Dirichlet distribution / Prior and posterior using the Dirichlet distribution
- structures / Learning structures
- structures, evaluating / Measures to evaluate structures
- structures, learning / Methods for learning structures, Advantages and limitations
- constraint-based techniques / Constraint-based techniques
- advantages and limitations / Advantages and limitations
- search and score-based techniques / Search and score-based techniques, How does it work?, Advantages and limitations

- lemmatization
- about / Stemming or lemmatization
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?

- Leveraging Bagging (LB) / Supervised learning experiments
- lexical features
- about / Lexical features
- character-based features / Character-based features
- word-based features / Word-based features
- part-of-speech tagging features / Part-of-speech tagging features
- taxonomy features / Taxonomy features

- lift curves / Gain charts and lift curves
- linear algorithm
- online linear models, with loss functions / Online linear models with loss functions
- Online Naive Bayes / Online Naïve Bayes

- Linear Embedding (LLE) / How does it work?
- linear models / Linear models
- Linear Regression / Linear Regression
- Naive Bayes / Naïve Bayes
- Logistic Regression / Logistic Regression
- about / Linear methods
- principal component analysis (PCA) / Principal component analysis (PCA)
- random projections (RP) / Random projections (RP)
- Multidimensional Scaling (MDS) / Multidimensional Scaling (MDS)

- Linear Regression
- about / Linear Regression
- algorithm input / Algorithm input and output
- algorithm output / Algorithm input and output
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Lloyds algorithm
- working / How does it work?

- Local Outlier Factor (LOF) / How does it work?
- logical datasets
- about / Training, validation, and test set

- Logistic Regression
- about / Logistic Regression
- algorithm input / Algorithm input and output
- algorithm output / Algorithm input and output
- working / How does it work?
- advantages / Advantages and limitations
- limitation / Advantages and limitations

## M

- machine learning / Machine Learning
- history / Machine learning – history and definition
- definition / Machine learning – history and definition
- relationship with / Machine learning – history and definition
- concepts / Machine learning – concepts and terminology
- terminology / Machine learning – concepts and terminology
- types / Machine learning – types and subtypes
- subtypes / Machine learning – types and subtypes
- supervised learning / Machine learning – types and subtypes
- semi-supervised learning / Machine learning – types and subtypes
- graph mining / Machine learning – types and subtypes
- probabilistic graph modeling / Machine learning – types and subtypes
- inferencing / Machine learning – types and subtypes
- time-series forecasting / Machine learning – types and subtypes
- association analysis / Machine learning – types and subtypes
- reinforcement learning / Machine learning – types and subtypes
- stream learning / Machine learning – types and subtypes
- incremental learning / Machine learning – types and subtypes
- datasets, used / Datasets used in machine learning
- practical issues / Practical issues in machine learning
- roles / Machine learning – roles and process
- process / Machine learning – roles and process
- tools / Machine learning – tools and datasets
- datasets / Machine learning – tools and datasets
- mapping / Machine learning mapping, Machine learning mapping
- in H2O / Machine learning in H2O
- future / The future of Machine Learning

- machine learning applications / Machine learning applications
- machine translation (MT) / Machine translation
- mallet
- topic modeling / Topic modeling with mallet

- Mallet
- URL / Machine learning – tools and datasets
- about / Machine learning – tools and datasets

- manifold learning
- about / Manifold learning
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- marginal distribution / Random variables, joint, and marginal distributions
- market data / Datasets used in machine learning
- Markov blanket / Markov blanket
- Markov chains
- about / Markov chains
- Hidden Markov models (HMM) / Hidden Markov models
- Hidden Markov models (HMM), portable path / Most probable path in HMM
- Hidden Markov models (HMM), posterior decoding / Posterior decoding in HMM

- Markov networks (MN) / Markov networks and conditional random fields
- representation / Representation
- parameterization / Parameterization
- Gibbs parameterization / Gibbs parameterization
- factor graphs / Factor graphs
- log-linear models / Log-linear models
- independencies / Independencies
- global / Global
- Pairwise Markov / Pairwise Markov
- Markov blanket / Markov blanket
- inference / Inference
- learning / Learning
- Conditional random fields (CRFs) / Conditional random fields

- Markov random field (MRF) / Markov networks and conditional random fields
- massively parallel processing (MPP) / Amazon Redshift
- Massive Online Analysis (MOA)
- about / Tools and software
- references / Tools and software
- reference link / Analysis of stream learning results

- mathematical transformation
- of feature / Outliers

- matrix
- about / Matrix
- transpose / Transpose of a matrix
- addition / Matrix addition
- scalar multiplication / Scalar multiplication
- multiplication / Matrix multiplication

- matrix product, properties
- about / Properties of matrix product
- linear transformation / Linear transformation
- matrix inverse / Matrix inverse
- eigendecomposition / Eigendecomposition
- positive definite matrix / Positive definite matrix

- maximum entropy Markov model (MEMM)
- about / Maximum entropy Markov models for NER
- input / Input and output
- output / Input and output
- working / How does it work?
- advantages / Advantages and limitations
- limitation / Advantages and limitations

- Maximum Likelihood Estimates (MLE) / How does it work?
- Maximum likelihood estimation (MLE) / Maximum likelihood estimation for Bayesian networks
- McNemars Test / McNemar's Test
- McNemar test / Comparing algorithms and metrics
- mean / Mean
- mean shift
- about / Mean shift
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- meta learners
- about / Ensemble learning and meta learners

- Micro Clustering based Algorithm (MCOD) / Micro Clustering based Algorithm (MCOD)
- Microsoft Azure HDInsight / Microsoft Azure HDInsight
- Min-Max Normalization / Outliers
- minimal redundancy maximal relevance (mRMR) / Minimal redundancy maximal relevance (mRMR)
- Minimum Covariant Determinant (MCD) / How does it work?
- Minimum Description Length (MDL) / How does it work?
- missing values
- handling / Handling missing values, Results, observations, and analysis

- Mixed National Institute of Standards and Technology (MNIST) / Data quality analysis
- Mldata.org
- about / Datasets
- URL / Datasets

- MNIST database
- reference link / Data collection

- MOA
- about / Machine learning – tools and datasets

- model
- building / Model building
- linear models / Linear models

- model assesment
- about / Model assessment, evaluation, and comparisons, Model assessment

- model comparison
- about / Model assessment, evaluation, and comparisons, Model comparisons
- algorithms, comparing / Comparing two algorithms
- multiple algorithms, comparing / Comparing multiple algorithms

- model evaluation
- about / Model assessment, evaluation, and comparisons

- model evaluation metrics
- about / Model evaluation metrics, Model evaluation metrics
- confusion matrix / Confusion matrix and related metrics
- PRC curve / ROC and PRC curves
- ROC curve / ROC and PRC curves
- Gain charts / Gain charts and lift curves
- lift curves / Gain charts and lift curves
- Confusion Metrics, evaluation / Evaluation on Confusion Metrics
- ROC curves / ROC Curves, Lift Curves, and Gain Charts
- Lift Curves / ROC Curves, Lift Curves, and Gain Charts
- Gain Charts / ROC Curves, Lift Curves, and Gain Charts

- model evolution, monitoring
- Kubat / Widmer and Kubat
- drift detection method (DDM) / Drift Detection Method or DDM
- early drift detection method (EDDM) / Early Drift Detection Method or EDDM

- model evolution, monitoring
- about / Monitoring model evolution
- Widmer / Widmer and Kubat
- early drift detection method (EEDM) / Early Drift Detection Method or EDDM

- modeling techniques
- linear algorithm / Linear algorithms
- non-linear algorithms / Non-linear algorithms
- ensemble algorithms / Ensemble algorithms
- partition based algorithm / Partition based
- hierarchical clustering / Hierarchical based and micro clustering
- micro clustering / Hierarchical based and micro clustering
- density based algorithm / Density based
- grid based algorithm / Grid based

- models
- non-linear models / Non-linear models
- ensemble learning / Ensemble learning and meta learners
- meta learners / Ensemble learning and meta learners
- clustering analysis / Observations and clustering analysis
- observations / Observations and clustering analysis

- model validation techniques
- about / Model validation techniques
- prequential evaluation / Prequential evaluation
- holdout evaluation / Holdout evaluation
- controlled permutations / Controlled permutations
- evaluation criteria / Evaluation criteria
- algorithms, versus metrics / Comparing algorithms and metrics

- most probable explanation (MPE) / MAP queries and marginal MAP queries
- Multi-layered neural network
- inputs / Inputs, neurons, activation function, and mathematical notation
- neuron / Inputs, neurons, activation function, and mathematical notation
- activation function / Inputs, neurons, activation function, and mathematical notation
- mathematical notation / Inputs, neurons, activation function, and mathematical notation
- about / Multi-layered neural network
- structure and mathematical notations / Structure and mathematical notations
- activation functions / Activation functions in NN
- training / Training neural network

- multi-layered perceptron (MLP) / Feature relevance and analysis
- Multi-layer feed-forward neural network
- about / Multi-layer feed-forward neural network

- multi-view SSL
- about / Co-training SSL or multi-view SSL

- Multidimensional Scaling (MDS)
- about / Multidimensional Scaling (MDS)
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- multinomial distribution / Random variables, joint, and marginal distributions
- multiple algorithms, comparing
- ANOVA test / ANOVA test
- Friedmans test / Friedman's test

- multivariate feature analysis
- about / Multivariate feature analysis
- scatter plots / Multivariate feature analysis
- ScatterPlot Matrix / Multivariate feature analysis
- parallel plots / Multivariate feature analysis

- multivariate feature selection
- about / Multivariate feature selection
- minimal redundancy maximal relevance (mRMR) / Minimal redundancy maximal relevance (mRMR)
- correlation-based feature selection (CFS) / Correlation-based feature selection (CFS)

## N

- Naive Bayes
- about / Naïve Bayes
- algorithm input / Algorithm input and output
- algorithm output / Algorithm input and output
- working / How does it work?
- advantages / Advantages and limitations
- limitation / Advantages and limitations

- Naive Bayes (NB) / Feature relevance and analysis
- named entity recognition / Information extraction and named entity recognition
- named entity recognition (NER)
- about / Named entity recognition
- Hidden Markov models / Hidden Markov models for NER
- Maximum entropy Markov model (MEMM) / Maximum entropy Markov models for NER

- natural language processing (NLP)
- about / NLP, subfields, and tasks, Deep learning and NLP
- text categorization / Text categorization
- part-of-speech tagging (POS tagging) / Part-of-speech tagging (POS tagging)
- text clustering / Text clustering
- information extraction / Information extraction and named entity recognition
- named entity recognition / Information extraction and named entity recognition
- sentiment analysis / Sentiment analysis and opinion mining
- opinion mining / Sentiment analysis and opinion mining
- coreference resolution / Coreference resolution
- Word sense disambiguation (WSD) / Word sense disambiguation
- machine translation (MT) / Machine translation
- semantic reasoning / Semantic reasoning and inferencing
- inferencing / Semantic reasoning and inferencing
- text summarization / Text summarization
- question, automating / Automating question and answers
- answers, automating / Automating question and answers

- Nemenyi test / Comparing algorithms and metrics
- Neo4J / Graph databases
- Neo4j
- about / Machine learning – tools and datasets
- URL / Machine learning – tools and datasets
- URL, for licensing / Machine learning – tools and datasets

- neural network, training
- about / Training neural network
- empirical risk minimization / Empirical risk minimization
- parameter initialization / Parameter initialization
- loss function / Loss function
- gradients / Gradients
- feed forward and backpropagation / Feed forward and backpropagation, How does it work?

- neural networks
- limitations / Limitations of neural networks, Vanishing gradients, local optimum, and slow training

- No Free Lunch Theorem (NFLT) / Model building
- non-linear algorithms
- about / Non-linear algorithms
- Hoeffding Trees (HT) / Hoeffding trees or very fast decision trees (VFDT)
- very fast decision trees (VFDT) / Hoeffding trees or very fast decision trees (VFDT)

- non-linear models
- about / Non-linear models
- Decision Trees / Decision Trees
- K-Nearest Neighbors (KNN) / K-Nearest Neighbors (KNN)
- support vector machines (SVM) / Support vector machines (SVM)

- non-negative Matrix factorization (NMF)
- about / Non-negative matrix factorization (NMF)
- input / Input and output
- output / Input and output
- working / How does it work?
- advantages / Advantages and limitations
- limitation / Advantages and limitations

- Non-negative Matrix Factorization (NNMF) / Clustering techniques
- nonlinear methods
- about / Nonlinear methods
- Kernel Principal Component Analysis (KPCA) / Kernel Principal Component Analysis (KPCA)
- manifold learning / Manifold learning

- normalization
- about / Outliers
- Min-Max Normalization / Outliers
- Z-Score Normalization / Outliers

- Normalized mutual information (NMI) / Normalized mutual information index
- NoSQL
- about / NoSQL
- key-value databases / Key-value databases
- document databases / Document databases
- columnar databases / Columnar databases
- graph databases / Graph databases

- notations, supervised learning
- about / Formal description and notation
- instance / Formal description and notation
- label / Formal description and notation
- binary classification / Formal description and notation
- regression / Formal description and notation
- dataset / Formal description and notation

- Noun Phrase (NP)
- about / Syntactic features

## O

- one-class SVM
- about / One-class SVM
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- online bagging algorithm
- about / Online Bagging algorithm
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- online boosting algorithm
- about / Online Boosting algorithm
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Online k-Means
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- online linear models, with loss functions
- inputs / Inputs and outputs
- ouputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitataions / Advantages and limitations

- Online Naive Bayes
- about / Online Naïve Bayes
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- OpenMarkov
- about / Machine learning – tools and datasets
- URL / Machine learning – tools and datasets

- opinion mining / Sentiment analysis and opinion mining
- OrientDB / Graph databases
- outlier algorithms
- about / Outlier algorithms
- statistical-based / Outlier algorithms, Statistical-based
- distance-based / Outlier algorithms
- density-based / Outlier algorithms
- clustering-based / Outlier algorithms
- high-dimension-based / Outlier algorithms
- distance-based methods / Distance-based methods
- density-based methods / Density-based methods
- clustering-based methods / Clustering-based methods
- high-dimensional-based methods / High-dimensional-based methods
- one-class SVM / One-class SVM

- outlier detection
- about / Outlier or anomaly detection
- outlier algorithms / Outlier algorithms
- outlier evaluation techniques / Outlier evaluation techniques
- used, for unsupervised learning / Unsupervised learning using outlier detection
- partition-based clustering / Partition-based clustering for outlier detection
- input / Inputs and outputs, Inputs and outputs
- output / Inputs and outputs, Inputs and outputs
- working / How does it work?, How does it work?
- advantages / Advantages and limitations, Advantages and limitations
- limitations / Advantages and limitations, Advantages and limitations
- Exact Storm / Exact Storm
- Abstract-C / Abstract-C
- Direct Update of Events (DUE) / Direct Update of Events (DUE)
- Micro Clustering based Algorithm (MCOD) / Micro Clustering based Algorithm (MCOD)
- Approx Storm / Approx Storm
- validation techniques / Validation and evaluation techniques
- evaluation techniques / Validation and evaluation techniques

- outlier evaluation techniques
- about / Outlier evaluation techniques
- supervised evaluation / Supervised evaluation
- unsupervised evaluation / Unsupervised evaluation
- technique / Unsupervised evaluation

- outlier models
- observation / Observations and analysis
- analysis / Observations and analysis

- outliers
- handling / Outliers
- detecting, in data / Outliers
- IQR / Outliers
- distance-based methods / Outliers
- density-based methods / Outliers
- mathematical transformation, of feature / Outliers
- handling, robust statistical algorithms used / Outliers

- oversampling / Undersampling and oversampling

## P

- Page-Hinckley test / CUSUM and Page-Hinckley test
- Paired-t test / Paired-t test
- pairwise-adaptive similarity / Pairwise-adaptive similarity
- parallel plots / Multivariate feature analysis
- Parquet / Columnar databases
- part-of-speech tagging (POS tagging) / Part-of-speech tagging (POS tagging)
- partial memory
- about / Partial memory
- full memory / Full memory
- detection methods / Detection methods
- adaptation methods / Adaptation methods

- partition based algorithm
- Online k-Means / Online k-Means

- peristalsis / Visualization analysis
- Phrase (VP)
- about / Syntactic features

- Pipelines
- reference link / Random Forest

- Poisson distribution / Poisson distribution
- Polynomial Kernel / How does it work?
- positive definite matrix / Positive definite matrix
- positive semi-definite matrix / Positive definite matrix
- PRC curve / ROC and PRC curves
- Prepositional Phrase (PP)
- about / Syntactic features

- principal component analysis (PCA)
- about / Principal component analysis (PCA)
- inputs / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advanatges / Advantages and limitations
- limitations / Advantages and limitations

- Principal Component Analysis (PCA) / Embedded approach
- principal components / How does it work?
- probabilistic graphical models (PGM) / Machine learning – tools and datasets
- probabilistic graph modeling / Machine learning – types and subtypes
- probabilistic latent semantic analysis (PLSA)
- about / Probabilistic latent semantic analysis (PLSA)
- input / Input and output
- output / Input and output
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- probabilistic latent semantic index (PLSI) / Topic modeling
- Probabilistic Principal Component Analysis (PPCA) / Advantages and limitations
- probability
- about / Probability revisited
- concepts / Concepts in probability
- conditional probability / Conditional probability
- Chain rule and Bayes' theorem / Chain rule and Bayes' theorem
- random variables / Random variables, joint, and marginal distributions
- joint / Random variables, joint, and marginal distributions
- marginal distributions / Random variables, joint, and marginal distributions
- marginal independence / Marginal independence and conditional independence
- conditional independence / Marginal independence and conditional independence
- factors / Factors
- factors, types / Factor types
- distribution queries / Distribution queries
- probabilistic queries / Probabilistic queries
- MAP queries / MAP queries and marginal MAP queries
- marginal MAP queries / MAP queries and marginal MAP queries

- process, machine learning
- about / Process
- business problem, identifying / Process
- mapping / Process
- data collection / Process
- data quality analysis / Process
- data sampling / Process
- transformation / Process
- feature analysis / Process
- feature selection / Process
- modeling / Process
- model evaluation / Process
- model selection / Process
- model deployment / Process
- model performance, monitoring / Process

- processors / SAMOA architecture
- Propagation-based techniques, Bayesian networks
- about / Propagation-based techniques
- belief propagation / Belief propagation
- factor graph / Factor graph
- factor graph, messaging in / Messaging in factor graph
- input and output / Input and output
- working / How does it work?
- advantages and limitations / Advantages and limitations

- publish-subscribe frameworks / Publish-subscribe frameworks

## Q

- Query by Committee (QBC)
- about / Query by Committee (QBC)

- Query by disagreement (QBD)
- about / Query by disagreement (QBD)

## R

- R-Squared / R-Squared
- Radial Basis Function (RBF) / Inputs and outputs
- Rand index / Rand index
- Random Forest / Random Forest
- Random Forest (RF) / Feature relevance and analysis, Random Forest
- random projections (RP)
- about / Random projections (RP)
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- RapidMiner
- about / Machine learning – tools and datasets, Case Study – Horse Colic Classification
- URL / Machine learning – tools and datasets
- experiments / RapidMiner experiments
- visualization analysis / Visualization analysis
- feature selection / Feature selection
- model process flow / Model process flow
- model evaluation metrics / Model evaluation metrics

- real-time Big Data Machine Learning
- about / Real-time Big Data Machine Learning
- SAMOA / SAMOA as a real-time Big Data Machine Learning framework
- machine learning algorithms / Machine Learning algorithms
- tools / Tools and usage
- usage / Tools and usage
- experiments / Experiments, results, and analysis
- results / Experiments, results, and analysis
- analysis / Experiments, results, and analysis
- results, analysis / Analysis of results

- real-time stream processing / Real-time stream processing
- real-world case study
- about / Real-world case study
- tools / Tools and software
- software / Tools and software
- business problem / Business problem
- machine learning, mapping / Machine learning mapping
- data collection / Data collection
- data quality analysis / Data quality analysis
- data sampling / Data sampling and transformation
- data transformation / Data sampling and transformation
- feature analysis / Feature analysis and dimensionality reduction
- dimensionality reduction / Feature analysis and dimensionality reduction
- models, clustering / Clustering models, results, and evaluation
- results / Clustering models, results, and evaluation, Outlier models, results, and evaluation
- evaluation / Clustering models, results, and evaluation, Outlier models, results, and evaluation
- outlier models / Outlier models, results, and evaluation

- reasoning, Bayesian networks
- patterns / Reasoning patterns
- causal or predictive reasoning / Causal or predictive reasoning
- evidential or diagnostic reasoning / Evidential or diagnostic reasoning
- intercausal reasoning / Intercausal reasoning
- combined reasoning / Combined reasoning

- receiver operating characteristics (ROC) / Machine learning – concepts and terminology
- Recurrent neural networks (RNN)
- about / Recurrent Neural Networks
- structure / Structure of Recurrent Neural Networks
- learning / Learning and associated problems in RNNs
- issues / Learning and associated problems in RNNs
- Long short term memory (LSTM) / Long Short Term Memory
- Gated Recurrent Units (GRUs) / Gated Recurrent Units

- regression
- about / Formal description and notation

- regularization
- about / Regularization
- L2 regularization / L2 regularization
- L1 regularization / L1 regularization

- reinforcement learning / Machine learning – types and subtypes
- representation, Bayesian networks
- about / Representation
- definition / Definition

- resampling / Is sampling needed?
- Resilient Distributed Datasets (RDD) / Spark architecture
- Restricted Boltzmann Machines (RBM)
- about / Restricted Boltzmann Machines
- definition and mathematical notation / Definition and mathematical notation
- Conditional distribution / Conditional distribution
- free energy / Free energy in RBM
- training / Training the RBM
- sampling / Sampling in RBM
- contrastive divergence / Contrastive divergence , How does it work?
- persistent contrastive divergence / Persistent contrastive divergence

- ROC curve / ROC and PRC curves
- roles, machine learning
- about / Roles
- business domain expert / Roles
- data engineer / Roles
- project manager / Roles
- data scientist / Roles
- machine learning expert / Roles

## S

- SAMOA
- about / Machine learning – tools and datasets, SAMOA as a real-time Big Data Machine Learning framework
- URL / Machine learning – tools and datasets
- architecture / SAMOA architecture

- sampling
- about / Machine learning – concepts and terminology, Sampling
- uniform random sampling / Machine learning – concepts and terminology
- stratified random sampling / Machine learning – concepts and terminology
- cluster sampling / Machine learning – concepts and terminology
- systematic sampling / Machine learning – concepts and terminology

- sampling-based techniques, Bayesian networks
- about / Sampling-based techniques
- forward sampling with rejection / Forward sampling with rejection, How does it work?

- Samza / SAMOA as a real-time Big Data Machine Learning framework
- scalar product
- of vectors / Scalar product of vectors

- ScatterPlot Matrix / Multivariate feature analysis
- scatter plots / Multivariate feature analysis
- self-organizing maps (SOM)
- about / Self-organizing maps (SOM)
- inputs / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- self-training SSL
- about / Self-training SSL
- inputs / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations
- Query by Committee (QBC) / Query by Committee (QBC)

- semantic features / Semantic features
- semantic reasoning / Semantic reasoning and inferencing
- semi-supervised learning / Machine learning – types and subtypes
- Semi-Supervised Learning (SSL)
- about / Semi-supervised learning
- representation / Representation, notation, and assumptions
- notation / Representation, notation, and assumptions
- assumptions / Representation, notation, and assumptions
- assumptions, to be true / Representation, notation, and assumptions
- techniques / Semi-supervised learning techniques
- self-training SSL / Self-training SSL
- multi-view SSL / Co-training SSL or multi-view SSL
- co-training SSL / Co-training SSL or multi-view SSL
- label SSL / Cluster and label SSL
- cluster SSL / Cluster and label SSL
- transductive graph label propagation / Transductive graph label propagation
- transductive SVM (TSVM) / Transductive SVM (TSVM)
- advanatages / Advantages and limitations
- disadvanatages / Advantages and limitations
- data distribution sampling / Data distribution sampling

- Semi-Supervised Learning (SSL), case study
- about / Case study in semi-supervised learning
- tools / Tools and software
- software / Tools and software
- business problem / Business problem
- machine learning, mapping / Machine learning mapping
- data collection / Data collection
- data quality, analysis / Data quality analysis
- data sampling / Data sampling and transformation
- data transformation / Data sampling and transformation
- datasets / Datasets and analysis
- datasets, analysis / Datasets and analysis
- feature analysis, results / Feature analysis results
- experiments / Experiments and results
- results / Experiments and results
- analysis / Analysis of semi-supervised learning

- sentiment analysis / Sentiment analysis and opinion mining
- sequential data / Datasets used in machine learning
- shrinking methods
- embedded approach / Embedded approach

- Sigmoid function / Sigmoid function
- Sigmoid Kernel / How does it work?
- Silhouettes index / Silhouette's index
- similarity measures
- about / Similarity measures
- Euclidean distance / Euclidean distance
- Cosine distance / Cosine distance
- pairwise-adaptive similarity / Pairwise-adaptive similarity
- extended Jaccard Coefficient / Extended Jaccard coefficient
- Dice coefficient / Dice coefficient

- singular value decomposition (SVD) / Dimensionality reduction, Singular value decomposition (SVD)
- Singular Value Decomposition (SVD) / Advantages and limitations
- sliding windows
- about / Sliding windows

- SMILE
- reference link / Tools and software

- Smile
- URL / Machine learning – tools and datasets
- about / Machine learning – tools and datasets

- software / Tools and software
- source-sink frameworks / Source-sink frameworks
- Spark-MLlib
- about / Machine learning – tools and datasets
- URL / Machine learning – tools and datasets

- Spark core, components
- Resilient Distributed Datasets (RDD) / Spark architecture
- Lineage graph / Spark architecture

- Spark MLlib
- used, as Big Data Machine Learning / Spark MLlib as Big Data Machine Learning platform
- architecture / Spark architecture
- machine learning / Machine Learning in MLlib
- tools / Tools and usage
- usage / Tools and usage
- experiments / Experiments, results, and analysis
- results / Experiments, results, and analysis
- analysis / Experiments, results, and analysis
- reference link / Experiments, results, and analysis
- k-Means / k-Means
- k-Means, with PCA / k-Means with PCA
- k-Means with PCA, bisecting / Bisecting k-Means (with PCA)
- Gaussian Mixture Model (GMM) / Gaussian Mixture Model
- Random Forest / Random Forest
- results, analysis / Analysis of results

- Spark SQL / Spark SQL
- Spark Streaming
- about / Real-time Big Data Machine Learning

- sparse coding
- about / Sparse coding

- spectral clustering
- about / Spectral clustering
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- SQL frameworks / SQL frameworks
- standard deviation / Standard deviation
- standardization
- about / Document collection and standardization
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?

- Statistical-based
- about / Statistical-based
- input / Inputs and outputs
- outputs / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- stemming / Stemming or lemmatization
- step execution mode / Amazon Elastic MapReduce
- Stochastic Gradient Descent (SGD)
- about / How does it work?

- stop words removal
- about / Stop words removal
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?

- stratified sampling / Stratified sampling
- stream / SAMOA architecture
- stream computational technique
- about / Basic stream processing and computational techniques, Stream computations
- frequency count / Stream computations
- point queries / Stream computations
- distinct count / Stream computations
- mean / Stream computations
- standard deviation / Stream computations
- correlation coefficient / Stream computations
- sliding windows / Sliding windows
- sampling / Sampling

- stream learning / Machine learning – types and subtypes
- stream learning, case study
- about / Case study in stream learning
- tools / Tools and software
- software / Tools and software
- business problem / Business problem
- machine learning, mapping / Machine learning mapping
- data collection / Data collection
- data sampling / Data sampling and transformation
- data transformation / Data sampling and transformation
- feature analysis / Feature analysis and dimensionality reduction
- dimensionality reduction / Feature analysis and dimensionality reduction
- models / Models, results, and evaluation
- results / Models, results, and evaluation
- evaluation / Models, results, and evaluation
- supervised learning experiments / Supervised learning experiments
- concept drift experiments / Concept drift experiments
- clustering experiments / Clustering experiments
- outlier detection experiments / Outlier detection experiments
- results, analysis / Analysis of stream learning results

- Stream Processing Engines (SPE) / Real-time stream processing
- stream processing technique
- about / Basic stream processing and computational techniques

- structured data
- sequential data / Datasets used in machine learning

- Structure Score Measure / Measures to evaluate structures
- subfields
- about / NLP, subfields, and tasks

- Subspace Outlier Detection (SOD) / How does it work?
- Sum of Squared Errors (SSE) / Clustering models, results, and evaluation, Experiments, results, and analysis
- supervised learning / Machine learning – types and subtypes
- experiments / Supervised learning experiments
- Weka, experiments / Weka experiments
- RapidMiner, experiments / RapidMiner experiments
- reference link / Results, observations, and analysis
- and unsupervised learning, common issues / Issues in common with supervised learning
- assumptions / Assumptions and mathematical notations
- mathematical notations / Assumptions and mathematical notations

- Support Vector Machines (SVM) / How does it work?
- support vector machines (SVM)
- about / Support vector machines (SVM)
- algorithm input / Algorithm inputs and outputs
- algorithm output / Algorithm inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Syntactic features
- about / Syntactic features

- Syntactic Language Models (SLM)
- about / Syntactic features

- Synthetic Minority Oversampling Technique (SMOTE) / Undersampling and oversampling

## T

- tasks
- about / NLP, subfields, and tasks

- Term Frequency (TF) / Term frequency (TF)
- term frequency (TF) / Frequency-based techniques
- term frequency-inverse document frequency (TF-IDF) / Term frequency-inverse document frequency (TF-IDF)
- text categorization
- about / Text categorization

- text clustering / Text clustering
- about / Text clustering
- feature transformation / Feature transformation, selection, and reduction
- selection / Feature transformation, selection, and reduction
- reduction / Feature transformation, selection, and reduction
- techniques / Clustering techniques
- evaluation / Evaluation of text clustering

- text mining
- topics / Topics in text mining
- categorization/classification / Text categorization/classification
- topic modeling / Topic modeling
- clustering / Text clustering
- named entity recognition (NER) / Named entity recognition
- Deep Learning / Deep learning and NLP
- NLP / Deep learning and NLP

- text processing components
- about / Text processing components and transformations
- document collection / Document collection and standardization
- standardization / Document collection and standardization
- tokenization / Tokenization
- stop words removal / Stop words removal
- lemmatization / Stemming or lemmatization
- local-global dictionary / Local/global dictionary or vocabulary?
- vocabulary / Local/global dictionary or vocabulary?
- feature extraction/generation / Feature extraction/generation
- feature representation / Feature representation and similarity
- similarity / Feature representation and similarity
- feature selection / Feature selection and dimensionality reduction
- dimensionality reduction / Feature selection and dimensionality reduction

- text summarization / Text summarization
- time-series forecasting / Machine learning – types and subtypes
- tokenization
- about / Tokenization
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?

- tools / Tools and software
- about / Tools and usage
- Mallet / Mallet
- KNIME / KNIME

- tools, machine learning
- RapidMiner / Machine learning – tools and datasets
- Weka / Machine learning – tools and datasets
- Knime / Machine learning – tools and datasets
- Mallet / Machine learning – tools and datasets
- Elki / Machine learning – tools and datasets
- JCLAL / Machine learning – tools and datasets
- KEEL / Machine learning – tools and datasets
- DeepLearning4J / Machine learning – tools and datasets
- Spark-MLlib / Machine learning – tools and datasets
- H2O / Machine learning – tools and datasets
- MOA/SAMOA / Machine learning – tools and datasets
- Neo4j / Machine learning – tools and datasets
- GraphX / Machine learning – tools and datasets
- OpenMarkov / Machine learning – tools and datasets
- Smile / Machine learning – tools and datasets

- topic modeling
- about / Topic modeling
- probabilistic latent semantic analysis (PLSA) / Probabilistic latent semantic analysis (PLSA)
- with mallet / Topic modeling with mallet
- business problem / Business problem
- machine learning, mapping / Machine Learning mapping
- data collection / Data collection
- data sampling / Data sampling and transformation
- transformation / Data sampling and transformation
- feature analysis / Feature analysis and dimensionality reduction
- dimensionality reduction / Feature analysis and dimensionality reduction
- models / Models, results, and evaluation
- results / Models, results, and evaluation
- evaluation / Models, results, and evaluation
- text processing results, analysis / Analysis of text processing results

- training phases
- competitive phase / How does it work?
- cooperation phase / How does it work?
- adaptive phase / How does it work?

- transaction data / Datasets used in machine learning
- transductive graph label propagation
- about / Transductive graph label propagation
- input / Inputs and outputs
- output / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- transductive SVM (TSVM)
- about / Transductive SVM (TSVM)
- output / Inputs and outputs
- input / Inputs and outputs
- working / How does it work?
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- transformations
- about / Text processing components and transformations

- Tree augmented network (TAN)
- about / Tree augmented network
- input and output / Input and output
- working / How does it work?
- advantages and limitations / Advantages and limitations

- Tunedit
- about / Datasets
- URL / Datasets

## U

- UCI repository
- reference link / Data Collection

- UC Irvine (UCI) database
- about / Datasets
- URL / Datasets

- uncertainty sampling
- about / Uncertainty sampling
- working / How does it work?
- least confident sampling / Least confident sampling
- smallest margin sampling / Smallest margin sampling
- label entropy sampling / Label entropy sampling
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- undersampling / Undersampling and oversampling
- univariate feature analysis
- about / Univariate feature analysis
- categorical features / Categorical features
- continuous features / Continuous features

- univariate feature selection
- information theoretic approach / Information theoretic approach
- statistical approach / Statistical approach

- unnormalized measure / Factor types
- unstructured data / Datasets used in machine learning
- mining, issues / Issues with mining unstructured data

- unsupervised learning / Machine learning – types and subtypes
- specific issues / Issues specific to unsupervised learning
- assumptions / Assumptions and mathematical notations
- mathematical notations / Assumptions and mathematical notations
- outlier detection, used / Unsupervised learning using outlier detection

- usage / Tools and usage
- US Forest Service (USFS) / Data collection
- US Geological Survey (USGS) / Data collection

## V

- V-Measure
- about / V-Measure
- Homogeneity / V-Measure
- Completeness / V-Measure

- validation
- techniques / Training, validation, and test set

- Variable elimination (VE) algorithm / Variable elimination algorithm
- variance / Variance
- vector
- about / Vector
- scalar product / Scalar product of vectors

- vector space model (VSM)
- about / Vector space model
- binary / Binary
- Term Frequency (TF) / Term frequency (TF)
- inverse document frequency (IDF) / Inverse document frequency (IDF)
- term frequency-inverse document frequency (TF-IDF) / Term frequency-inverse document frequency (TF-IDF)

- version space sampling
- about / Version space sampling
- Query by disagreement (QBD) / Query by disagreement (QBD)

- very fast decision trees (VFDT) / Hoeffding trees or very fast decision trees (VFDT)
- output / Inputs and outputs
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Very Fast K-means Algorithm (VFKM) / Advantages and limitations
- visualization analysis
- about / Visualization analysis
- univariate feature analysis / Univariate feature analysis
- multivariate feature analysis / Multivariate feature analysis

- Vote Entropy
- disadvanatages / How does it work?

## W

- weighted linear sum (WLS) / How does it work?
- weighted linear sum of squares (WSS) / How does it work?
- weighted majority algorithm (WMA)
- about / Weighted majority algorithm
- input / Inputs and outputs
- output / Inputs and outputs
- working / Advantages and limitations
- advantages / Advantages and limitations
- limitations / Advantages and limitations

- Weka
- URL / Machine learning – tools and datasets
- about / Machine learning – tools and datasets, Case Study – Horse Colic Classification
- experiments / Weka experiments
- Sample end-to-end process, in Java / Sample end-to-end process in Java
- experimenter / Weka experimenter and model selection
- model selection / Weka experimenter and model selection

- Weka Bayesian Network GUI / Weka Bayesian Network GUI
- Welchs test / Welch's t test
- Widmer / Widmer and Kubat
- Wilcoxon signed-rank test / Wilcoxon signed-rank test
- Word sense disambiguation (WSD) / Word sense disambiguation
- wrapper approach / Wrapper approach

## Z

- Z-Score Normalization / Outliers
- ZeroMQ Message Transfer Protocol (ZMTP) / Message queueing frameworks