Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Covers eigenvalues, eigenfunctions, Hermitian operators, and the measurement of observables in quantum chemistry.
Covers advanced numerical analysis topics including deep neural networks and optimization methods.
Introduces functions in Python, covering predefined and user-defined functions, formal and effective parameters, and the importance of docstrings.
Explores turbulence characteristics, simulation methods, and modeling challenges, providing guidelines for choosing and validating turbulence models.
Covers the basics of working with text files in C programming, including opening, reading, writing, and closing files, with examples and demonstrations.
Discusses the reactive hard spheres model, collision cross sections, reaction cross sections, and two-body classical scattering.
