Holiday
DELTA台達202 W3W4F3F4
This course aims to introduce convex analysis and optimization. Over the last two decades, the powerful convex optimization theory and tools have been extensively used for solving wide range of cutting edge optimization problems in sciences and engineering, such as (a) multiple-input multiple- output (MIMO) wireless communications and networking for 5G-beyong and 6G, (b) blind source separation (BSS) for biomedical and hyperspectral image analysis, and (c) machine learning (ML). In particular, many ML methods (e.g., Lasso, and Support Vector Machines) are ba<x>sed on the identification of some parameters by minimizing an ob<x>jective function, defined by the sum of a loss function and some regularization terms (e.g., linear regression, and L1-norm regularization). The resulting optimization problem can be optimally solved through reformulation into a convex problem in many instances, thus naturally forming a strong li<x>nk and mutual need between ML a
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Average Percentage 59.22
Std. Deviation 27.81
非常態開設課程。
電機系大學部3年級4年級,電資院學士班大學部3年級4年級優先,第3次選課起開放全校修習
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