Modern Machine Learning and Pattern Recognition by Bouchaffra, Djamel;

Long description:

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Modern Machine Learning and Pattern Recognition presents a rigorous, comprehensive exploration from classical learning paradigms to the latest deep architectures and large language models. Integrating supervised, unsupervised, self-supervised, and reinforcement learning with modern neural network design, the book offers a unified view of machine learning and pattern recognition grounded in statistical learning theory and optimization. Through a progression of chapters, readers move from foundations and multilayer perceptrons to convolutional and recurrent networks, generative adversarial models, and transformer-based large language models.

A special feature of this text is its combination of theoretical depth with extensive practice-oriented material, including many exercises, Python-based projects, and real-world case studies that bridge mathematical analysis with implementation and experimentation. Beyond just standard architectures, the book introduces original coalitional neural models with energy-based foundations, drawing on statistical physics, game theory, and random matrix theory to analyze and redesign deep networks at a fundamental level. It concludes with dedicated chapters on the ethical and social implications of large-scale models and on emerging research directions such as topological datat analysis, meta-reasoning in LLMs, and causal inference: helping readers connect core techniques to current debates and future developments in AI.

Meant for advanced undergraduates, graduate students, researchers, and professionals, this single-author monograph provides a coherent and pedagogically structured treatment suitable for classroom adoption, self-study, and reference. Readers are equipped not only to understand existing models, but also to engage with ongoing research on interpretability, robustness, and the next generation of learning architectures.

Table of Contents:

.- Foundations of Machine Learning.
.- Fundamentals of Neural Networks.
.- Deep Learning Models.
.- Convolutional Neural Networks (CNNs).
.- Recurrent Neural Networks and Long Short-Term Memory (LSTM).
.- Generative Adversarial Networks (GANs).
.- Transformer-based Large Language Models.
.- Training Transformer Models.
.- Coalitional Neural Models with Energy-Based Foundations.
.- Ethical Implications of Language Models.
.- Future Directions of Machine Learning.
.- Conclusion and Perspectives.