+- MKLab (https://mklab.gr)
+-- Forum: [INDEX] (https://mklab.gr/forumdisplay.php?fid=1)
+--- Forum: MATHEMATICS (https://mklab.gr/forumdisplay.php?fid=3)
+---- Forum: BOOKS (https://mklab.gr/forumdisplay.php?fid=6)
+----- Forum: APPLIED MATHEMATICS (https://mklab.gr/forumdisplay.php?fid=19)
+------ Forum: OPTIMIZATION (https://mklab.gr/forumdisplay.php?fid=55)
+------ Thread: EE 227C Convex Optimization and Approximation (/showthread.php?tid=391)
EE 227C Convex Optimization and Approximation - mklabgr - 06-16-2026
EE 227C Convex Optimization and Approximation
Summary
The EE227C course site contains graduate-level course notes and materials on convex optimization and approximation, focusing on how optimization theory is used in machine learning and modern data analysis. It presents a structured introduction to continuous optimization methods, including gradient-based algorithms, stochastic optimization, duality, non-convex optimization, and higher-order methods, while also emphasizing computational efficiency, robustness, and practical implementation.
The materials are organized as lecture-style notes paired with Python/Jupyter notebooks that illustrate theoretical concepts through experiments. Overall, the course aims to bridge rigorous mathematical optimization theory with real-world applications in data science, showing how modern algorithms for learning and large-scale optimization can be derived, analyzed, and applied in practice.
COURSE PAGE