Numerical Recipes (PHYS10090)
Physics and Astronomy
Normal Year Taken
Delivery Session Year
The aim of this course is to develop an understanding of numerical algorithms, how they are implemented, and how to use them to solve practical numerical problems using standard Python libraries such as SciPy.
This course is taught through a combination of lectures and hands- on programming exercises. Python will be used as the programming language for this course. Proficiency in Python is assumed (this is not a Python programming course).The course material will include:- Linear algebra (solving linear systems of equations, diagonalization)- Optimization (finding minima and maxima of real-valued functions)- Parameter fitting to data sets (Chi squared, maximum likelihood, Bayesianinference)- Random number generation- Non-linear root finding- Monte Carlo methods- Integration- Differential equations (ordinary and partial)- Discrete Fourier Transform- Principles of machine learning
Written Exam 0%, Coursework 100%, Practical Exam 0%
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