Convex optimization theory with applications in signal processing, finance, and machine learning: portfolio optimization, filter/beamforming design, classification methods, circuit design, robust designs under uncertainty, sparse optimization, low-rank optimization, image processing, graph learning from data, discrete maximum likelihood decoding, network optimization, distributed algorithms, Internet protocol design, etc. For PG students in second year or above.