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Smart city: Difference between revisions
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b) The idea can also be an algorithm or module on how to improve the performance of the architecture. | b) The idea can also be an algorithm or module on how to improve the performance of the architecture. | ||
Learning about such a fast-moving field is an exciting opportunity, but covering it in a traditional course setting comes with some caveats you should be aware of. | |||
* No canonical curriculum: Many topics in mathematics and computer science such as linear algebra, real analysis, discrete mathematics, data structures and algorithms, etc come with well-established curricula; courses on such subjects can be found at most universities, and they tend to cover similar topics in a similar order. This is not the case for emerging research areas like deep learning: the set of topics to be covered, as well as the order and way of thinking about each topic, has not yet been perfected. | |||
* Few learning materials: There are very few high-quality textbooks or other learning materials that synthesize or explain much of the content we will cover. In many cases, '''the research paper that introduced an idea is the best or only resource for learning about it'''. | |||
* Theory lags experiments: At present, '''video analytics is primarily an empirically driven research field'''. We may use mathematical notation to describe or communicate our algorithms and ideas, and many techniques are motivated by some mathematical or computational intuition, but in most cases, we rely on experiments rather than formal proofs to determine the scenarios where one technique might outperform another. This can sometimes be unsettling for students, as the question “why does that work?” may not always have a precise, theoretically-grounded answer. | |||
* Things will change: If you were to study deep learning ten years from now, it is very likely that it will look quite different from today. There may be new fundamental discoveries or new ways of thinking about things we already know; there may be some ideas we think are important today, that will turn out in retrospect not to have been. There may be similarly impactful results lurking right around the corner. | |||
==Prerequisites== | ==Prerequisites== | ||