This highly-rated course boasts a 0.0-star-star rating from 0 reviews and has successfully guided 1,002 students in mastering Data Science skills. Featuring 18 hour(s) of expert-led content delivered in English, this course offers thorough training to enhance your Social Science expertise. The course details were last updated on December 24, 2024. This coupon code is brought to you by Anonymous.
- Expired on November 13, 2025
- Last Update: November 13, 2025
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Price:
54.99 $14.99 $
About This Course
Convex optimization is a cornerstone of modern applied mathematics, underpinning a wide range of technologies from machine learning and artificial intelligence to signal processing, control systems, and operations research. This course offers a deep and structured exploration of large-scale convex optimization, tailored for learners who seek both theoretical rigor and practical insight.
Through 55 carefully crafted video lectures, this course guides you from the foundational concepts of convex sets and functions to advanced algorithmic techniques for solving high-dimensional optimization problems. It is designed to be accessible to motivated learners while maintaining the depth expected in graduate-level education. This course:
Offers a structured deep dive into large-scale convex optimization, balancing theory and algorithmic practice.
Introduces core concepts: convex sets, convex functions, and their properties—essential for high-dimensional optimization.
Begins with real-world applications: shortest-path problems, signal/image processing, and support vector machines.
Covers mathematical foundations: inner-product spaces, convex geometry, convexity-preserving operations, differentiability, lower semicontinuity, and closedness.
Focuses on algorithmic techniques: Subgradient method: convergence, boundedness, and implementation; and Forward-backward splitting and accelerated variants for composite problems.
Explores duality theory: Primal-dual relationships; Perturbation and infimal value functions; Fenchel and Lagrange duality frameworks.
Equips learners to analyze, solve, and implement convex optimization methods in practical and research contexts.
By the end of this course, you will:
Understand the theoretical underpinnings of convex optimization
Be able to formulate and solve large-scale convex problems
Implement key algorithms for optimization in practical settings
Apply convex optimization to real-world problems in engineering and data science
Be prepared for advanced studies or research in optimization and applied mathematics
Why This Course Stands Out
University-Level Instruction: Based on a graduate course taught at a leading European engineering faculty, ensuring academic depth and clarity.
Balanced Approach: Combines intuitive explanations with formal mathematical rigor, making it suitable for both practitioners and researchers.
Algorithmic Focus: Emphasizes practical methods for solving optimization problems, with step-by-step walkthroughs of algorithms and their convergence properties.
Real-World Relevance: Demonstrates how convex optimization is used in cutting-edge fields like machine learning, data science, and engineering.
