EC ENGR 133A
Applied Numerical Computing
Description: Lecture, four hours; discussion, one hour; outside study, seven hours. Enforced requisites: course 131A, and Civil Engineering M20 or Computer Science 31 or Mechanical and Aerospace Engineering M20. Introduction to numerical computing/analysis; analytic formulations versus numerical solutions; floating-point representations and rounding errors. Review of MATLAB; mathematical software. Linear equations; LU factorization; bounds on error; iterative methods for solving linear equations; conditioning and stability; complexity. Interpolation and approximation; splines. Zeros and roots of nonlinear equations. Linear least squares and orthogonal (QR) factorization; statistical interpretation. Numerical optimization; Newton method; nonlinear least squares. Numerical quadrature. Solving ordinary differential equations. Eigenvalues and singular values; QR algorithm; statistical applications. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Fall 2024 - One of the most useful and applicable courses that I have taken in UCLA. It is pretty much an advanced linear algebra course, but much less theoretical than Math 115A. Professor Vandenberghe is the goat, he is extremely knowledgeable and patient when explaining concepts. Homework questions can be tough and tedious, but are thought-provoking and satisfying once solved. Midterm was manageable, but the final was a genocide. Fortunately, the curve was amazing, so everyone's grades got boosted significantly. Overall, brilliant experience and I would highly recommend (especially for those planning to dive deep into Robotics/AI).
Fall 2024 - One of the most useful and applicable courses that I have taken in UCLA. It is pretty much an advanced linear algebra course, but much less theoretical than Math 115A. Professor Vandenberghe is the goat, he is extremely knowledgeable and patient when explaining concepts. Homework questions can be tough and tedious, but are thought-provoking and satisfying once solved. Midterm was manageable, but the final was a genocide. Fortunately, the curve was amazing, so everyone's grades got boosted significantly. Overall, brilliant experience and I would highly recommend (especially for those planning to dive deep into Robotics/AI).
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Most Helpful Review
Winter 2024 - Lin Yang is a great Professor. I'd recommend this class if you are looking for a light workload with engaging numerical computing material. This class had 1 quiz every week which were very easy as the professor would help out and allow students to exchange ideas. The midterm was also very fair. The group project is fun because you apply what you learned in class. The homework was also very easy as it would take about 1-2 hours maximum for each homework.
Winter 2024 - Lin Yang is a great Professor. I'd recommend this class if you are looking for a light workload with engaging numerical computing material. This class had 1 quiz every week which were very easy as the professor would help out and allow students to exchange ideas. The midterm was also very fair. The group project is fun because you apply what you learned in class. The homework was also very easy as it would take about 1-2 hours maximum for each homework.
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