References
by Krishna Choppella, Dr. Uday Kamath, Boštjan Kaluža, Jennifer L. Reese, Richard M
Machine Learning: End-to-End guide for Java developers
- Machine Learning: End-to-End guide for Java developers
- Table of Contents
- Machine Learning: End-to-End guide for Java developers
- Credits
- Preface
- 1. Module 1
- 1. Getting Started with Data Science
- Problems solved using data science
- Understanding the data science problem - solving approach
- Acquiring data for an application
- The importance and process of cleaning data
- Visualizing data to enhance understanding
- The use of statistical methods in data science
- Machine learning applied to data science
- Using neural networks in data science
- Deep learning approaches
- Performing text analysis
- Visual and audio analysis
- Improving application performance using parallel techniques
- Assembling the pieces
- Summary
- 2. Data Acquisition
- 3. Data Cleaning
- 4. Data Visualization
- 5. Statistical Data Analysis Techniques
- 6. Machine Learning
- 7. Neural Networks
- 8. Deep Learning
- 9. Text Analysis
- 10. Visual and Audio Analysis
- 11. Mathematical and Parallel Techniques for Data Analysis
- 12. Bringing It All Together
- 1. Getting Started with Data Science
- 2. Module 2
- 1. Applied Machine Learning Quick Start
- 2. Java Libraries and Platforms for Machine Learning
- 3. Basic Algorithms – Classification, Regression, and Clustering
- 4. Customer Relationship Prediction with Ensembles
- 5. Affinity Analysis
- 6. Recommendation Engine with Apache Mahout
- 7. Fraud and Anomaly Detection
- 8. Image Recognition with Deeplearning4j
- 9. Activity Recognition with Mobile Phone Sensors
- 10. Text Mining with Mallet – Topic Modeling and Spam Detection
- 11. What is Next?
- A. References
- 3. Module 3
- 1. Machine Learning Review
- Machine learning – history and definition
- What is not machine learning?
- Machine learning – concepts and terminology
- Machine learning – types and subtypes
- Datasets used in machine learning
- Machine learning applications
- Practical issues in machine learning
- Machine learning – roles and process
- Machine learning – tools and datasets
- Summary
- 2. Practical Approach to Real-World Supervised Learning
- Formal description and notation
- Data transformation and preprocessing
- Feature relevance analysis and dimensionality reduction
- Model building
- Model assessment, evaluation, and comparisons
- Case Study – Horse Colic Classification
- Summary
- References
- 3. Unsupervised Machine Learning Techniques
- Issues in common with supervised learning
- Issues specific to unsupervised learning
- Feature analysis and dimensionality reduction
- Clustering
- Outlier or anomaly detection
- Real-world case study
- Summary
- References
- 4. Semi-Supervised and Active Learning
- Semi-supervised learning
- Active learning
- Case study in active learning
- Summary
- References
- 5. Real-Time Stream Machine Learning
- Assumptions and mathematical notations
- Basic stream processing and computational techniques
- Concept drift and drift detection
- Incremental supervised learning
- Incremental unsupervised learning using clustering
- Modeling techniques
- Partition based
- Hierarchical based and micro clustering
- Density based
- Grid based
- Validation and evaluation techniques
- Key issues in stream cluster evaluation
- Evaluation measures
- Cluster Mapping Measures (CMM)
- V-Measure
- Other external measures
- Unsupervised learning using outlier detection
- Case study in stream learning
- Summary
- References
- 6. Probabilistic Graph Modeling
- Probability revisited
- Graph concepts
- Bayesian networks
- Representation
- Inference
- Learning
- Learning parameters
- Learning structures
- Measures to evaluate structures
- Methods for learning structures
- Constraint-based techniques
- Search and score-based techniques
- 7. Deep Learning
- Multi-layer feed-forward neural network
- Inputs, neurons, activation function, and mathematical notation
- Multi-layered neural network
- Structure and mathematical notations
- Activation functions in NN
- Training neural network
- Empirical risk minimization
- Parameter initialization
- Loss function
- Gradients
- Feed forward and backpropagation
- How does it work?
- Regularization
- L2 regularization
- L1 regularization
- Limitations of neural networks
- Deep learning
- Building blocks for deep learning
- Rectified linear activation function
- Restricted Boltzmann Machines
- Autoencoders
- Unsupervised pre-training and supervised fine-tuning
- Deep feed-forward NN
- Deep Autoencoders
- Deep Belief Networks
- Deep learning with dropouts
- Sparse coding
- Convolutional Neural Network
- CNN Layers
- Recurrent Neural Networks
- Building blocks for deep learning
- Case study
- Summary
- References
- 8. Text Mining and Natural Language Processing
- NLP, subfields, and tasks
- Text categorization
- Part-of-speech tagging (POS tagging)
- Text clustering
- Information extraction and named entity recognition
- Sentiment analysis and opinion mining
- Coreference resolution
- Word sense disambiguation
- Machine translation
- Semantic reasoning and inferencing
- Text summarization
- Automating question and answers
- Issues with mining unstructured data
- Text processing components and transformations
- Topics in text mining
- Tools and usage
- Summary
- References
- NLP, subfields, and tasks
- 9. Big Data Machine Learning – The Final Frontier
- What are the characteristics of Big Data?
- Big Data Machine Learning
- Batch Big Data Machine Learning
- Case study
- Business problem
- Machine Learning mapping
- Data collection
- Data sampling and transformation
- Spark MLlib as Big Data Machine Learning platform
- A. Linear Algebra
- B. Probability
- D. Bibliography
- Index
- Empirical risk minimization
- Multi-layer feed-forward neural network
- Modeling techniques
- 1. Machine Learning Review
- G. Cormode and S. Muthukrishnan (2010). An improved data stream summary: The Count-Min sketch and its applications. Journal of Algorithms, 55(1):58–75, 2005.
- João Gama (2010). Knowledge Discovery from Data Streams, Chapman and Hall / CRC Data Mining and Knowledge Discovery Series, CRC Press 2010, ISBN 978-1-4398-2611-9, pp. I-XIX, 1-237.
- B. Babcock, M. Datar, R. Motwani (2002). Sampling from a moving window over streaming data, in Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms, pp.633–634, 2002.
- Bifet, A. and Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. In Proceedings of SIAM int. conf. on Data Mining. SDM. 443–448.
- Vitter, J. (1985). Random sampling with a reservoir. ACM Trans. Math. Softw. 11, 1, 37–57.
- Gama, J., Medas, P., Castillo, G., and Rodrigues, P. (2004). Learning with drift detection. In Proceedings of the 17th Brazilian symp. on Artif. Intell. SBIA. 286–295.
- Gama, J., Sebastiao, R., and Rodrigues, P. 2013. On evaluating stream learning algorithms. Machine Learning 90, 3, 317–346.
- Domingos, P. and Hulten, G. (2000). Mining high-speed data streams. In Proceedings. of the 6th ACM SIGKDD int. conference on Knowledge discovery and data mining. KDD. 71–80.
- Oza, N. (2001). Online ensemble learning. Ph.D. thesis, University of California Berkeley.
- Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A. (2014). A Survey on Concept Drift Adaptation.ACM Computing Surveys 46(4), Article No. 44.
- Farnstrom, F., Lewis, J., and Elkan, C. (2000). Scalability for clustering algorithms revisited. SIGKDD Exploration, 51–57.
- Zhang, T., Ramakrishnan, R., and Livny, M. (1996). BIRCH: An Efficient Data Clustering Method for Very Large Databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, 103–114.
- Aggarwal, C. (2003). A Framework for Diagnosing Changes in Evolving Data Streams. In ACM SIGMOD Conference. 575–586.
- Chen, Y. and Tu, L. (2007). Density-based clustering for real-time stream data. In KDD '07: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, 133–142.
- Kremer, H., Kranen, P., Jansen, T., Seidl, T., Bifet, A., Holmes, G., and Pfahringer, B. (2011). An effective evaluation measure for clustering on evolving data streams. In proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. KDD '11. ACM, New York, NY, USA, 868–876.
- Mahdiraji, A. R. (2009). Clustering data stream: A survey of algorithms. International Journal of Knowledge-Based and Intelligent Engineering Systems, 39–44.
- F. Angiulli and F. Fassetti (2007). Detecting distance-based outliers in streams of data. In proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, CIKM '07, pages 811–820, New York, NY, USA, 2007. ACM.
- D. Yang, E. A. Rundensteiner, and M. O. Ward (2009). Neighbor-based pattern detection for windows over streaming data. In Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT '09, pages 529–540, New York, NY, USA, 2009. ACM.
- M. Kontaki, A. Gounaris, A. Papadopoulos, K. Tsichlas, and Y. Manolopoulos (2011). Continuous monitoring of distance-based outliers over data streams. In Data Engineering (ICDE), 2011 IEEE 27th International Conference on, pages 135–146, April 2011.
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