Methodology bias is difficult to avoid, since we can only see the methodologies that have been developed to proof of concept. Time is a continuous horizon of bias breaking in research. ARPAnet, which is now the Internet, was designed to reduce the bias by narrowing the gap between research laboratories, but it does not bridge across time.
Luger's book is the more modern approach of the two, but still dated, which shows the age of Russell and Norvig's work. In a field that changes so quickly, neither are textbooks of choice for artificial intelligence survey courses at the most cutting edge universities today. There are less biased textbooks from that period too, such as Artificial Intelligence, by Patrick Henry Winston, 1992, Addison-Wesley.
MIT's Artificial Intelligence Lab (course 6.034) formerly used Russell and Norvig, as recently as 2012, but the course is now taught by Winston and uses a publicly available updated extension of his textbook. The original Winston textbook's coverage is more comprehensive than the union of the two textbooks mentioned in the question, even before the updates made by MIT faculty and researchers.
Textbooks are most valuable when the development of a new foundation for a field has solidified and research shifts into what Thomas Kuhn termed normal science, where post-graduate level work hammers out the details of the edges of the field. Once that occurs, the foundation can be codified in standard problems and solutions.
The state of AI research is currently following what Kuhn called a paradigm shift, but the foundations of AI are not nearly at rest. Winston, recognizing this, decided to use a web reference for the course and continuously update it, with the help of PhD candidates, laboratory staff, and associated faculty.
For a more recent mathematical treatment specific to machine learning, Foundations of Machine Learning by Mohri, Rostamizadeh, and Talwalkar, 2012, MIT Press is an excellent work for those with sufficient background to follow the mathematics. It covers probability distributions and expectation in a formal way and using the more recent nomenclature and terminology.
For instance, the second chapter presents a probabilistic framework they call the PAC Learning Framework, which relates certainty, allowable approximation, and sample size requirements across learning approaches.