TOC:
- Introduction
- Machine-Learning
Foundations: The Probabilistic Framework
- Probabilistic Modeling
& Inference: Examples
- Machine Learning
Algorithms
- Neural Networks:
The Theory
- Neural Networks:
Applications
- Hidden Markov Models:
The Theory
- Hidden Markov Models:
Applications
- Probabilistic Graphical
Models in Bioinformatics
- Probabilistic Models
of Evolution: Phylogenetic
- Stochastic Grammars
& Linguistics
- Microarrays &
Gene Expression
- Internet Resources
& Public Databases
A. Statistics
B. Information Theory, Entropy, & Relative
C. Probabilistic Graphical Models
D. HMM Technicalities, Scaling, Periodic
E. Gaussian Processes, Kernel Methods, and Support Vector Machines
F. Symbols and Abbreviations
References Index
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