EC ENGR 133A
Mathematics of Design
Description: (Formerly numbered Electrical Engineering 133A.) 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 2017 - Very interesting class if you are into linear algebra and MATLAB, that was the cool part. It's also a pain in the ass, homework took me anywhere from 5 to 15 hours to complete each time. Lectures are hard to keep up with, because the professor mumbles and often seems uninterested. For example about seeming uninterested, on the first day of class he suggested that if you are genuinely interested in the subject that you should drop out of the class and take it with the other prof named Vandenberghe. I think he is sort of overrated on this website, not sure how. Anticipating I will get a B, not sure how the curve for tests goes.
Fall 2017 - Very interesting class if you are into linear algebra and MATLAB, that was the cool part. It's also a pain in the ass, homework took me anywhere from 5 to 15 hours to complete each time. Lectures are hard to keep up with, because the professor mumbles and often seems uninterested. For example about seeming uninterested, on the first day of class he suggested that if you are genuinely interested in the subject that you should drop out of the class and take it with the other prof named Vandenberghe. I think he is sort of overrated on this website, not sure how. Anticipating I will get a B, not sure how the curve for tests goes.
Most Helpful Review
Fall 2018 - This was an extremely challenging class. Probably the most challenging among the core upper division EE classes. The class consisted of 7 homework assignments, a midterm, and a final. The homeworks were very tricky and took a lot of time to understand. Most of them took upwards of 6 hours to complete (in my and many others' experiences). Many of the homework questions are just impossible to understand without knowing some trick that you wouldn't think of immediately. Going to TA office hours and discussions are necessary if you want to do well on the homeworks. Course hero was also an extremely great resource because many homework questions are recycled from previous years, so they can be found there. Vandenberghe's lectures were quite dry. They mainly consisted of him reading off of his prepared lecture slides and writing some annotations on the chalkboard. He is pretty monotone and tends to mumble a bit. However, Vandenberghe is an extremely great guy with a good sense of humor. He is very concerned about the students' success and is very helpful in office hours. I would give him a soft recommendation as a professor for this class. The material was extremely uninteresting in the beginning. It mainly consisted of a review of concepts from Math 33a (definitions of vectors, matrices, and operations). However, it got much more interesting later on as we talked more about regression and how the concepts we were learning were applied to fields like machine learning, image processing, and more. While the material is quite dry at times, it is beneficial and useful to know if you have any interest in continuing studies in Machine Learning and related fields.
Fall 2018 - This was an extremely challenging class. Probably the most challenging among the core upper division EE classes. The class consisted of 7 homework assignments, a midterm, and a final. The homeworks were very tricky and took a lot of time to understand. Most of them took upwards of 6 hours to complete (in my and many others' experiences). Many of the homework questions are just impossible to understand without knowing some trick that you wouldn't think of immediately. Going to TA office hours and discussions are necessary if you want to do well on the homeworks. Course hero was also an extremely great resource because many homework questions are recycled from previous years, so they can be found there. Vandenberghe's lectures were quite dry. They mainly consisted of him reading off of his prepared lecture slides and writing some annotations on the chalkboard. He is pretty monotone and tends to mumble a bit. However, Vandenberghe is an extremely great guy with a good sense of humor. He is very concerned about the students' success and is very helpful in office hours. I would give him a soft recommendation as a professor for this class. The material was extremely uninteresting in the beginning. It mainly consisted of a review of concepts from Math 33a (definitions of vectors, matrices, and operations). However, it got much more interesting later on as we talked more about regression and how the concepts we were learning were applied to fields like machine learning, image processing, and more. While the material is quite dry at times, it is beneficial and useful to know if you have any interest in continuing studies in Machine Learning and related fields.
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