Following are resources a Physics Undergraduate might appreciate. Some of these are standard texts,
others are for additional exploration. In the classroom, I do not use these as ‘prescribed textbooks’ , though I have borrowed (consciously or subliminally) from them, especially Problems.
Mechanics
- An Introduction to Mechanics: Daniel Kleppner, Robert Kolenkow (for Problems)
- Introduction to Classical Mechanics with Problems and Solutions: David Morin (Mostly for Problems, but also for interesting discussion on the Work-Energy Theorem and its generalization)
- Classical Mechanics (The Theoretical Minimum): Leonard Susskind (to be used as an additional textbook, apart from lectures, for advanced undergraduate students)
Statistical Physics
- Fundamentals of Statistical and Thermal Physics: Frederick Reif (excellent collection of Problems)
- Statistical and Thermal Physics (With Computer Applications): Harvey Gould and Ian Tobochnik (good in general as a textbook and in particular for computational explorations)
- Entropy, Order Parameter and Complexity: James P. Sethna (for additional exploration and interesting applications and Problems, for advanced undergraduates)
- Thermal Physics: Concepts and Practice: Allen L. Wasserman (good reference for applications and Problems)
- Statistical Mechanics: Algorithms and Computations: Werner Krauth (for computational exploration. There is also a Coursera course to go with the book).
Quantum Mechanics
- A Modern Approach to Quantum Mechanics: John S. Townsend (good as an undergraduate textbook)
- Quantum Mechanics (The Theoretical Minimum): Leonard Susskind (excellent as a complementary textbook for undergraduates)
- Lectures on Quantum Theory: Mathematical and Structural Foundations: Chris J Isham (for advanced undergraduates who want to explore the general structure and mathematical foundations)
Mathematical Physics
- Mathematical Methods for Physicists: Arfken, Weber and Harris
- Mathematical Tools for Physics: James Nearing
- Mathematical Methods for Physics and Engineering: Riley, Hobson, Bence
Bayesian Inference
- Practical Bayesian Inference: Coryn A.L. Bailer-Jones (Introductory text for undergraduates, includes code in the R programming language)
- Statistical Rethinking: Richard McElreath (Excellent text, code in R)
- Think Bayes: Allen B. Downey (for exploring Bayes Theorem through Python code)