Informatics and Machine Learning From Martingales to Metaheuristics 1st Edition by Winters Hilt Stephen ISBN 1119716748 9781119716747

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ISBN 10: 1119716748
ISBN 13: 9781119716747
Author: Winters Hilt Stephen

Informatics and Machine Learning

Discover a thorough exploration of how to use computational, algorithmic, statistical, and informatics methods to analyze digital data

Informatics and Machine Learning: From Martingales to Metaheuristics delivers an interdisciplinary presentation on how analyze any data captured in digital form. The book describes how readers can conduct analyses of text, general sequential data, experimental observations over time, stock market and econometric histories, or symbolic data, like genomes. It contains large amounts of sample code to demonstrate the concepts contained within and assist with various levels of project work.

The book offers a complete presentation of the mathematical underpinnings of a wide variety of forms of data analysis and provides extensive examples of programming implementations. It is based on two decades worth of the distinguished author’s teaching and industry experience.

A thorough introduction to probabilistic reasoning and bioinformatics, including Python shell scripting to obtain data counts, frequencies, probabilities, and anomalous statistics, or use with Bayes’ rule

An exploration of information entropy and statistical measures, including Shannon entropy, relative entropy, maximum entropy (maxent), and mutual information

A practical discussion of ad hoc, ab initio, and bootstrap signal acquisition methods, with examples from genome analytics and signal analytics

Perfect for undergraduate and graduate students in machine learning and data analytics programs, Informatics and Machine Learning: From Martingales to Metaheuristics will also earn a place in the libraries of mathematicians, engineers, computer scientists, and life scientists with an interest in those subjects.

Informatics and Machine Learning From Martingales to Metaheuristics 1st Edition Table of contents:

1 Introduction

1.1 Data Science: Statistics, Probability, Calculus … Python (or Perl) and Linux

1.2 Informatics and Data Analytics

1.3 FSA‐Based Signal Acquisition and Bioinformatics

1.4 Feature Extraction and Language Analytics

1.5 Feature Extraction and Gene Structure Identification

1.6 Theoretical Foundations for Learning

1.7 Classification and Clustering

1.8 Search

1.9 Stochastic Sequential Analysis (SSA) Protocol (Deep Learning Without NNs)

1.10 Deep Learning using Neural Nets

1.11 Mathematical Specifics and Computational Implementations

2 Probabilistic Reasoning and Bioinformatics

2.1 Python Shell Scripting

2.2 Counting, the Enumeration Problem, and Statistics

2.3 From Counts to Frequencies to Probabilities

2.4 Identifying Emergent/Convergent Statistics and Anomalous Statistics

2.5 Statistics, Conditional Probability, and Bayes’ Rule

2.6 Emergent Distributions and Series

2.7 Exercises

3 Information Entropy and Statistical Measures

3.1 Shannon Entropy, Relative Entropy, Maxent, Mutual Information

3.2 Codon Discovery from Mutual Information Anomaly

3.3 ORF Discovery from Long‐Tail Distribution Anomaly

3.4 Sequential Processes and Markov Models

3.5 Exercises

4 Ad Hoc, Ab Initio, and Bootstrap Signal Acquisition Methods

4.1 Signal Acquisition, or Scanning, at Linear Order Time‐Complexity

4.2 Genome Analytics: The Gene‐Finder

4.3 Objective Performance Evaluation: Sensitivity and Specificity

4.4 Signal Analytics: The Time‐Domain Finite State Automaton (tFSA)

4.5 Signal Statistics (Fast): Mean, Variance, and Boxcar Filter

4.6 Signal Spectrum: Nyquist Criterion, Gabor Limit, Power Spectrum

4.7 Exercises

5 Text Analytics

5.1 Words

5.2 Phrases – Short (Three Words)

5.3 Phrases – Long (A Line or Sentence)

5.4 Exercises

6 Analysis of Sequential Data Using HMMs

6.1 Hidden Markov Models (HMMs)

6.2 Graphical Models for Markov Models and Hidden Markov Models

6.3 Standard HMM Weaknesses and their GHMM Fixes

6.4 Generalized HMMs (GHMMs – “Gems”): Minor Viterbi Variants

6.5 HMM Implementation for Viterbi (in C and Perl)

6.6 Exercises

7 Generalized HMMs (GHMMs)

7.1 GHMMs: Maximal Clique for Viterbi and Baum–Welch

7.2 GHMMs: Full Duration Model

7.3 GHMMs: Linear Memory Baum–Welch Algorithm

7.4 GHMMs: Distributable Viterbi and Baum–Welch Algorithms

7.5 Martingales and the Feasibility of Statistical Learning (further details in Appendix)

7.6 Exercises

8 Neuromanifolds and the Uniqueness of Relative Entropy

8.1 Overview

8.2 Review of Differential Geometry [206, 207]

8.3 Amari’s Dually Flat Formulation [113–115]

8.4 Neuromanifolds [113–115]

8.5 Exercises

9 Neural Net Learning and Loss Bounds Analysis

9.1 Brief Introduction to Neural Nets (NNs)

9.2 Variational Learning Formalism and Use in Loss Bounds Analysis

9.3 The “sinh−1(ω)” link algorithm (SA)

9.4 The Loss Bounds Analysis for sinh−1(ω)

9.5 Exercises

10 Classification and Clustering

10.1 The SVM Classifier – An Overview

10.2 Introduction to Classification and Clustering

10.3 Lagrangian Optimization and Structural Risk Minimization (SRM)

10.4 SVM Binary Classifier Implementation

10.5 Kernel Selection and Tuning Metaheuristics

10.6 SVM Multiclass from Decision Tree with SVM Binary Classifiers

10.7 SVM Multiclass Classifier Derivation (Multiple Decision Surface)

10.8 SVM Clustering

10.9 Exercises

11 Search Metaheuristics

11.1 Trajectory‐Based Search Metaheuristics

11.2 Population‐Based Search Metaheuristics

11.3 Exercises

12 Stochastic Sequential Analysis (SSA)

12.1 HMM and FSA‐Based Methods for Signal Acquisition and Feature Extraction

12.2 The Stochastic Sequential Analysis (SSA) Protocol

12.3 Channel Current Cheminformatics (CCC) Implementation of the Stochastic Sequential Analysis (SSA) Protocol

12.4 SCW for Detector Sensitivity Boosting

12.5 SSA for Deep Learning

12.6 Exercises

13 Deep Learning Tools – TensorFlow

13.1 Neural Nets Review

13.2 TensorFlow from Google

13.3 Exercises

14 Nanopore Detection – A Case Study

14.1 Standard Apparatus

14.2 Controlling Nanopore Noise Sources and Choice of Aperture

14.3 Length Resolution of Individual DNA Hairpins

14.4 Detection of Single Nucleotide Differences (Large Changes in Structure)

14.5 Blockade Mechanism for 9bphp

14.6 Conformational Kinetics on Model Biomolecules

14.7 Channel Current Cheminformatics

14.8 Channel‐Based Detection Mechanisms

14.9 The NTD Nanoscope

14.10 NTD Biosensing Methods

14.11 Exercises

Appendix A: Python and Perl System Programming in Linux

A.1 Getting Linux and Python in a Flash (Drive)

A.2 Linux and the Command Shell

A.3 Perl Review: I/O, Primitives, String Handling, Regex

Appendix B: Physics

B.1 The Calculus of Variations

Appendix C: Math

C.1 Martingales [102]

C.2 Hoeffding Inequality

References

Index

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Tags: Winters Hilt Stephen, Machine Learning, Martingales, Metaheuristics