# Statistical Machine Learning: A Unified Framework

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- eBook:Statistical Machine Learning: A Unified Framework
- Author:Richard Golden
- Edition:1 edition
- Categories:
- Data:July 2, 2020
- ISBN:1138484695
- ISBN-13:9781138484696
- Language:English
- Pages:524 pages
- Format:PDF

**Book Description**

**Statistical Machine Learning: A Unified Framework**provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms.

**Features:**

- Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms
- Matrix calculus methods for supporting machine learning analysis and design applications
- Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions
- Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification

**Content**

1. A Statistical Machine Learning Framework

2. Set Theory for Concept Modeling

3. Formal Machine Learning Algorithms

II Deterministic Learning Machines

4. Linear Algebra for Machine Learning

5. Matrix Calculus for Machine Learning

6. Convergence of Time-Invariant Dynamical Systems

7. Batch Learning Algorithm Convergence

III Stochastic Learning Machines

8. Random Vectors and Random Functions

9. Stochastic Sequences

10. Probability Models of Data Generation

11. Monte Carlo Markov Chain Algorithm Convergence

12. Adaptive Learning Algorithm Convergence

IV Generalization Performance

14. Simulation Methods for Evaluating Generalization

15. Analytic Formulas for Evaluating Generalization

16. Model Selection and Evaluation

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