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
- D. Bell and H. Wang (2000). A Formalism for Relevance and its Application in Feature Subset Selection. Machine Learning, 41(2):175–195.
- J. Doak (1992). An Evaluation of Feature Selection Methods and their Application to Computer Security. Technical Report CSE–92–18, Davis, CA: University of California, Department of Computer Science.
- M. Ben-Bassat (1982). Use of Distance Measures, Information Measures and Error Bounds in Feature Evaluation. In P. R. Krishnaiah and L. N. Kanal, editors, Handbook of Statistics, volume 2, pages 773–791, North Holland.
- Littlestone N, Warmuth M (1994) The weighted majority algorithm. Information Computing 108(2):212–261
- Breiman L., Friedman J.H., Olshen R.A., Stone C.J. (1984) Classification and Regression Trees, Wadsforth International Group.
- B. Ripley(1996), Pattern recognition and neural networks. Cambridge University Press, Cambridge.
- Breiman, L., (1996). Bagging Predictors, Machine Learning, 24 123-140.
- Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. 2(2):1-47.
- Bouckaert, R. (2004), Naive Bayes Classifiers That Perform Well with Continuous Variables, Lecture Notes in Computer Science, Volume 3339, Pages 1089 – 1094.
- Aha D (1997). Lazy learning, Kluwer Academic Publishers, Dordrecht
- Nadeau, C. and Bengio, Y. (2003), Inference for the generalization error. In Machine Learning 52:239– 281.
- Quinlan, J.R. (1993). C4.5: Programs for machine learning, Morgan Kaufmann, San Francisco.
- Vapnik, V. (1995), The Nature of Statistical Learning Theory. Springer Verlag.
- Schapire RE, Singer Y, Singhal A (1998). Boosting and Rocchio applied to text filtering. In SIGIR '98: Proceedings of the 21st Annual International Conference on Research and Development in Information Retrieval, pp 215–223
- Breiman L.(2001). Random Forests. Machine Learning, 45 (1), pp 5-32.
- Nathalie Japkowicz and Mohak Shah (2011). Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press.
- Hanley, J. & McNeil, B. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36.
- Tjen-Sien, L., Wei-Yin, L., Yu-Shan, S. (2000). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms. Machine Learning 40: 203–228.
- A. W. Moore and M. S. Lee (1994). Efficient Algorithms for Minimizing Cross Validation Error. In Proc. of the 11th Int. Conf. on Machine Learning, pages 190–198, New Brunswick, NJ. Morgan Kaufmann.
- Nitesh V. Chawla et. al. (2002). Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research. 16:321-357.
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