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
- Daphne Koller and Nir Friedman (2009). Probabilistic Graphical Models. MIT Press. ISBN 0-262-01319-3.
- T. Verma and J. Pearl (1988), In proceedings for fourth workshop on Uncertainty in Artificial Intelligence, Montana, Pages 352-359. Causal Networks- Semantics and expressiveness.
- Dagum, P., and Luby, M. (1993). Approximating probabilistic inference in Bayesian belief networks is NP hard. Artificial Intelligence 60(1):141–153.
- U. Bertele and F. Brioschi, Nonserial Dynamic Programming, Academic Press. New York, 1972.
- Shenoy, P. P. and G. Shafer (1990). Axioms for probability and belief-function propagation, in Uncertainty in Artificial Intelligence, 4, 169-198, North-Holland, Amsterdam
- Bayarri, M.J. and DeGroot, M.H. (1989). Information in Selection Models. Probability and Bayesian Statistics, (R. Viertl, ed.), Plenum Press, New York.
- Spiegelhalter and Lauritzen (1990). Sequential updating of conditional probabilities on directed graphical structures. Networks 20. Pages 579-605.
- David Heckerman, Dan Geiger, David M Chickering (1995). In journal of Machine Learning. Learning Bayesian networks: The combination of knowledge and statistical data.
- Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, 131– 163.
- Isham, V. (1981). An introduction to spatial point processes and Markov random fields. International Statistical Rewview, 49(1):21–43
- Frank R. Kschischang, Brendan J. Frey, and Hans-Andrea Loeliger, Factor graphs and sum-product algorithm, IEEE Trans. Info. Theory, vol. 47, pp. 498–519, Feb. 2001.
- Kemeny, J. G. and Snell, J. L. Finite Markov Chains. New York: Springer-Verlag, 1976.
- Baum, L. E.; Petrie, T. (1966). Statistical Inference for Probabilistic Functions of Finite State Markov Chains. The Annals of Mathematical Statistics. 37 (6): 1554–1563.
- Gelman, A., Hwang, J. and Vehtar, A. (2004). Understanding predictive information criteria for Bayesian models. Statistics and Computing Journal 24: 997. doi:10.1007/s11222-013-9416-2
- Dimitris. Margaritis (2003). Learning Bayesian Network Model Structure From Data. Ph.D Thesis Carnegie Mellon University.
- John Lafferty, Andrew McCallum, Fernando C.N. Pereira (2001). Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, International Conference on Machine Learning 2001 (ICML 2001), pages 282-289.
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.