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Scala for Machine Learning

By : Patrick R. Nicolas
Book Image

Scala for Machine Learning

By: Patrick R. Nicolas

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Scala for Machine Learning
Patrick R. Nicolas
Dec, 2014 | 624 pages
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Table of Contents (20 chapters)
Scala for Machine Learning
Scala for Machine Learning
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started
Getting Started
Mathematical notation for the curious
Why machine learning?
Why Scala?
Model categorization
Taxonomy of machine learning algorithms
Don't reinvent the wheel!
Tools and frameworks
Source code
Let's kick the tires
Summary
2
Hello World!
Hello World!
Modeling
Defining a methodology
Monadic data transformation
A workflow computational model
Profiling data
Assessing a model
Summary
3
Data Preprocessing
Data Preprocessing
Time series in Scala
Moving averages
Fourier analysis
The discrete Kalman filter
Alternative preprocessing techniques
Summary
4
Unsupervised Learning
Unsupervised Learning
Clustering
Dimension reduction
Performance considerations
Summary
5
Naïve Bayes Classifiers
Naïve Bayes Classifiers
Probabilistic graphical models
Naïve Bayes classifiers
The Multivariate Bernoulli classification
Naïve Bayes and text mining
Pros and cons
Summary
6
Regression and Regularization
Regression and Regularization
Linear regression
Regularization
Numerical optimization
Logistic regression
Summary
7
Sequential Data Models
Sequential Data Models
Markov decision processes
The hidden Markov model
Conditional random fields
Regularized CRFs and text analytics
Comparing CRF and HMM
Performance consideration
Summary
8
Kernel Models and Support Vector Machines
Kernel Models and Support Vector Machines
Kernel functions
Support vector machines
Support vector classifiers – SVC
Anomaly detection with one-class SVC
Support vector regression
Performance considerations
Summary
9
Artificial Neural Networks
Artificial Neural Networks
Feed-forward neural networks
The multilayer perceptron
Evaluation
Convolution neural networks
Benefits and limitations
Summary
10
Genetic Algorithms
Genetic Algorithms
Evolution
Genetic algorithms and machine learning
Genetic algorithm components
Implementation
GA for trading strategies
Advantages and risks of genetic algorithms
Summary
11
Reinforcement Learning
Reinforcement Learning
Reinforcement learning
Learning classifier systems
Summary
12
Scalable Frameworks
Scalable Frameworks
An overview
Scala
Scalability with Actors
Akka
Apache Spark
Summary
Basic Concepts
Basic Concepts
Scala programming
Mathematics
Finances 101
Suggested online courses
References
Index
Index

The hidden Markov model

The hidden Markov model has numerous applications related to speech recognition, face identification (biometrics), and pattern recognition in pictures and videos [7:3].

A hidden Markov model consists of a Markov process (also known as a Markov chain) for observations with a discrete time. The main difference with the Markov processes is that the states are not observable. A new observation is emitted with a probability known as the emission probability each time the state of the system or model changes.

There are two sources of randomness, which are as follows:

  • Transition between states

  • Emission of an observation when a state is given

Let's reuse the boxes and balls example. If the boxes are hidden states (nonobservable), then the user draws the balls whose color is not visible. The emission probability is the probability bik =p(ot = colork|qt =Si) to retrieve a ball of the color k from a hidden box I, as shown in the following diagram:

The hidden Markov model for the balls...

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