nehpest wrote:Spring Quarter 2012 - mired in junior-level EEng. This will start in about 4 weeks.
ECE 302 - Electromagnetic Fields. Maxwell’s equations and electromagnetic concepts. Introduction to static and time varying fields; plane waves, boundary conditions, and transmission line equations. Applications to analog and digital circuits. 4 lectures/problem-solving.
ECE 307 - Network Analysis III. Frequency selective and two-port networks in the complex frequency domain. Fourier series and fourier transforms with applications to circuit analysis. Product fee required. 3 lectures/problem-solving.
ECE 315 - Probability, Statistics, and Random Processes for Electrical and Computer Engineering. Concept of probability, statistics, random variables, and random processes. Analysis of random signals through linear time invariant systems. 4 lectures/problem-solving.
ECE 542 - Digital Image Processing. Basic concepts in digital image processing such as point, algebraic, geometric operations, discrete Fourier transforms, and wavelet transforms, and applications such as image restoration, image compression, and pattern recognition. 4 lectures/problem-solving. Prerequisite: upper division courses in probability theory and digital signal processing.
I'm stoked about image processing - the professor is personable and knowledgeable, and the material is going to be challenging; as an added bonus, I'm pretty sure I can get elective credit for it. I'm waitlisting EM Fields, and might not get it (which sucks, as it's a critical path course for me). I'm decidedly UNexcited about the probability class, but that's largely because of the reputation the instructor has around the department.
I almost forgot the online courses I'm taking!
CS 373 - Udacity - Programming A Robotic Car. This class, taught by one of the foremost experts in AI, will teach you basic methods in Artificial Intelligence, including: probabilistic inference, computer vision, machine learning, and planning, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars. You will get a chance to visit, via video, the leading research labs in the field, and meet the scientists and engineers who are building self-driving cars at Stanford and Google.Prerequisites: The instructor will assume solid knowledge of programming, all programming will be in Python. Knowledge of probability and linear algebra will be helpful.
6.002x - MITx - 6.002 is designed to serve as a first course in an undergraduate electrical engineering (EE), or electrical engineering and computer science (EECS) curriculum. At MIT, 6.002 is in the core of department subjects required for all undergraduates in EECS. The course introduces the fundamentals of the lumped circuit abstraction. Topics covered include: resistive elements and networks; independent and dependent sources; switches and MOS transistors; digital abstraction; amplifiers; energy storage elements; dynamics of first- and second-order networks; design in the time and frequency domains; and analog and digital circuits and applications. Design and lab exercises are also significant components of the course.
Both are online; both should be awesome learning opportunities