This tutorial investigates the convergence of statistical mechanics and learning theory, elucidating the potential enhancements in machine learning methodologies through the integration of foundational principles from physics. The tutorial delves into advanced techniques like entropy, free energy, and variational inference which are utilized in machine learning, illustrating their significant contributions to model efficiency and robustness. By bridging these scientific disciplines, we aspire to inspire newer methodologies in researches, demonstrating how an in-depth comprehension of physical systems' behavior can yield more effective and dependable machine learning models, particularly in contexts characterized by uncertainty.

In this paper, deep neural network (DNN) is integrated with spatial modulation-orthogonal frequency division multiplexing (SM-OFDM) technique for end-to-end data detection over Rayleigh fading channel. This proposed system directly demodulates the received symbols, leaving the channel estimation done only implicitly. Furthermore, an ensemble network is also proposed for this system. Simulation results show that the proposed DNN detection scheme has a significant advantage over classical methods when the pilot overhead and cyclic prefix (CP) are reduced, owing to its ability to learn and adjust to complicated channel conditions. Finally, the ensemble network is shown to improve the generalization of the proposed scheme, while also showing a slight improvement in its performance.

This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in "big data" as well as "sparse data" problems. The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data.

Dynamical ML is machine learning that can adapt to variations over time; it requires "real-time recursive" learning algorithms and time-varying data models such as the ones described in the blog, Generalized Dynamical Machine Learning. In the process of DYNAMICAL machine learning (DML) applied to industrial IoT, the data model and the algorithms used (Generalized Dynamical Machine Learning) naturally generates what is called the "State-space" model of the machine. It may not *look* like the machine but it captures the dynamics in all its detail (there can be challenges in relating "states" to actual machine components though). I am a proponent of using the "State-space representation" that we get for FREE in Dynamical ML as the "digital twin". This is a topic of current exploration and advancement.

Dynamical ML is machine learning that can adapt to variations over time; it requires "real-time recursive" learning algorithms and time-varying data models such as the ones described in the blog, Generalized Dynamical Machine Learning. In the process of DYNAMICAL machine learning (DML) applied to industrial IoT, the data model and the algorithms used (Generalized Dynamical Machine Learning) naturally generates what is called the "State-space" model of the machine. It may not *look* like the machine but it captures the dynamics in all its detail (there can be challenges in relating "states" to actual machine components though). I am a proponent of using the "State-space representation" that we get for FREE in Dynamical ML as the "digital twin". This is a topic of current exploration and advancement.