**Machine Learning**

by Abdelhamid Mellouk, Abdennacer Chebira

**Publisher**: InTech 2009**ISBN-13**: 9789537619561**Number of pages**: 450

**Description**:

Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, 3d shape classification and retrieval, genetic network programming with reinforcement learning, heuristic dynamic programming, and more.

Download or read it online for free here:

**Download link**

(PDF)

## Similar books

**A Brief Introduction to Machine Learning for Engineers**

by

**Osvaldo Simeone**-

**arXiv.org**

This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.

(

**4092**views)

**Introduction to Machine Learning**

by

**Amnon Shashua**-

**arXiv**

Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).

(

**19219**views)

**Statistical Learning and Sequential Prediction**

by

**Alexander Rakhlin, Karthik Sridharan**-

**University of Pennsylvania**

This text focuses on theoretical aspects of Statistical Learning and Sequential Prediction. The minimax approach, which we emphasize throughout the course, offers a systematic way of comparing learning problems. We will discuss learning algorithms...

(

**4649**views)

**Modeling Agents with Probabilistic Programs**

by

**Owain Evans, et al.**-

**AgentModels.org**

This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning. One motivation is to create richer models of human planning, which capture human biases. The book assumes basic programming experience.

(

**3780**views)