- Joined
- 8/9/12
- Messages
- 5
- Points
- 11
I am registering for graduate classes in my computer science program. My goal is to work in quantitative finance as a quant trader or analyst. Machine Learning and Numerical Analysis and Differential Equations is offered during the same time in the fall semester. I plan to take Numerical Analysis: Linear and Nonlinear Equations in the spring. The following are the course descriptions.
Machine Learning: Introduces the fundamental set of techniques and algorithms that constitute machine learning as of today.
Numerical Analysis and Differential Equations: Introduction to the fundamentals of numerical analysis: error analysis, approximation, interpolation, numerical integration. In the second half of the course, the above are used to build approximate solvers for ordinary and partial differential equations. Strong emphasis is placed on understanding the advantages, disadvantages, and limits of applicability for all the covered techniques. Computer programming is required to test the theoretical concepts throughout the course.
Numerical Analysis: Linear and Nonlinear Equations: Introduction to the fundamentals of numerical linear algebra: direct and iterative methods for linear systems, eigenvalue problems, singular value decomposition. In the second half of the course, the above are used to build iterative methods for nonlinear systems and for multivariate optimization. Strong emphasis is placed on understanding the advantages, disadvantages, and limits of applicability for all the covered techniques. Computer programming is required to test the theoretical concepts throughout the course.
Which course should I take in the fall and why? Since I am planning to take Numerical Analysis in the spring, should I take Machine Learning in the fall, or would it benefit me more to take both Numerical courses?
Machine Learning: Introduces the fundamental set of techniques and algorithms that constitute machine learning as of today.
Numerical Analysis and Differential Equations: Introduction to the fundamentals of numerical analysis: error analysis, approximation, interpolation, numerical integration. In the second half of the course, the above are used to build approximate solvers for ordinary and partial differential equations. Strong emphasis is placed on understanding the advantages, disadvantages, and limits of applicability for all the covered techniques. Computer programming is required to test the theoretical concepts throughout the course.
Numerical Analysis: Linear and Nonlinear Equations: Introduction to the fundamentals of numerical linear algebra: direct and iterative methods for linear systems, eigenvalue problems, singular value decomposition. In the second half of the course, the above are used to build iterative methods for nonlinear systems and for multivariate optimization. Strong emphasis is placed on understanding the advantages, disadvantages, and limits of applicability for all the covered techniques. Computer programming is required to test the theoretical concepts throughout the course.
Which course should I take in the fall and why? Since I am planning to take Numerical Analysis in the spring, should I take Machine Learning in the fall, or would it benefit me more to take both Numerical courses?