Re:Carnegie Mellon Launches Undergraduate Degree in Artifici...
by PascLeRasc on Thursday May 10, 2018 @09:56PM
(#17044458)
So essentially what we need is an "academic CSEE" degree, where you replace courses on industrial topics like application design and databases / circuits and electromagnetics with these theory-based classes from both departments.
I'd also suggest adding some systems neuroscience courses in there too.
Re:Carnegie Mellon Launches Undergraduate Degree in Artifici...
by madengr on Friday May 11, 2018 @08:26AM
(#17046743)
Electromagnetics is all theory, so much so it’s been dropped from most EE curriculums for being to difficult.
Re:Carnegie Mellon Launches Undergraduate Degree in Artifici...
by PascLeRasc on Friday May 11, 2018 @08:38AM
(#17046835)
I wish my school would have done that, those were terribly intensive and not really stimulating like signals was.
Re:Carnegie Mellon Launches Undergraduate Degree in Artifici...
by madengr on Friday May 11, 2018 @12:16PM
(#17048527)
I ate it up, but then I’m an RF/Microwave EE. Grad level EM is even worse.
Re:Carnegie Mellon Launches Undergraduate Degree in Artifici...
by moultano on Friday May 11, 2018 @12:52AM
(#17045033)
I think you could get away with swapping out upper level algorithms and systems courses and swapping in statistics and ML. I've used my OS class 0 times in my career. The class I've used the most was the second level statistics class I took for my econ minor.
Re:Carnegie Mellon Launches Undergraduate Degree in Artifici...
by PascLeRasc on Friday May 11, 2018 @08:37AM
(#17046828)
definitely, and your algorithms class was probably industry-driven and an exposure to what you should be doing to write code in the workforce, not teaching you to come up with novel algorithms.
Yeah, not really. A lot of day-to-day work in ML requires rudimentary math, at most. I know PhDs who quickly get discouraged with ML because they're suddenly spending 95% of their time doing the grunt work. It would be a boon if we could hire non-PhDs who are competent in the fundamentals of signals, algorithms, statistics, and experiment design.
If you're aspiring to work in ML, what major do you choose now? Statistics? EE? CS? Math?
None of these are ideal. If you're doing CS, you're probably too busy with compilers or DB courses to get a proper education in signal processing, information theory, stochastic processes, etc. If you're in EE, you're too busy soldering circuits and to learn about data structures, algorithms, or software engineering courses. There's a lot of room for improvement here.
Even at the graduate level, most of our EE interns and new hires can't solve FizzBuzz, while the CS majors can't properly design a scientific experiment to save their lives.