- #1
TLeit
- 10
- 2
I previously inquired about whether a Probability or Mathematical Statistics course would be a more useful base for learning more about advanced quantum mechanics and related subjects in the future, since I need to take one more math elective next semester for my major. From the two I decided Probability would be better, but I have since been recommended to a few other classes that also sound interesting. I have posted the descriptions below. Any advice on which courses would be best for learning about quantum mechanics and similar fields in the future would be greatly appreciated! I am currently still leaning towards taking Probability.
Numerical Analysis I: Nonlinear equations, interpolation and approximation, least squares, systems of linear equations, and error analysis.
Mathematical Modeling: Introduction to building mathematical models in an applied context, including principles of modeling; project(s) involve modeling open-ended real-world problems. Skills covered may include discrete dynamical systems, differential equations, stochastic models, and linear programming.
Digital Image Processing: Applications of Fourier analysis and wavelets to optics and image processing. Topics include: diffraction, wave optical theory of lenses and imaging, wavelets, and image processing.
Probability: Probability in discrete and continuous sample spaces; conditional probability; counting techniques; probability functions; binomial, Poisson, normal distributions; and transformations of variables.
Numerical Analysis I: Nonlinear equations, interpolation and approximation, least squares, systems of linear equations, and error analysis.
Mathematical Modeling: Introduction to building mathematical models in an applied context, including principles of modeling; project(s) involve modeling open-ended real-world problems. Skills covered may include discrete dynamical systems, differential equations, stochastic models, and linear programming.
Digital Image Processing: Applications of Fourier analysis and wavelets to optics and image processing. Topics include: diffraction, wave optical theory of lenses and imaging, wavelets, and image processing.
Probability: Probability in discrete and continuous sample spaces; conditional probability; counting techniques; probability functions; binomial, Poisson, normal distributions; and transformations of variables.