Explore the dynamic field of online convex optimization with the second edition of this essential graduate-level textbook from The Bookish Owl. This revised edition delves into optimization as a continuous process, ideal for complex real-world scenarios where traditional models fall short. Blending mathematical optimization, game theory, and learning theory, this book offers a powerful machine learning framework that adapts and learns from experience. Discover new chapters on boosting, adaptive regret, and approachability, alongside expanded content and practical applications in areas like portfolio selection, recommendation systems, and SVM training. Perfect for advanced students and researchers seeking to master this critical area of AI and machine learning.