11th Exercise, Optimization for Machine Learning, Sose 2023, LMU Munich скачать в хорошем качестве

11th Exercise, Optimization for Machine Learning, Sose 2023, LMU Munich

All teaching material is available at: [github](https://github.com/bengsV/OptML) This video is the 11th exercise session for the Optimization for Machine Learning course (Summer Semester 2023) at LMU Munich, led by Viktor Bengs. In this session, a mock exam is discussed which covers most of the topics discussed in the course. The session provides a comprehensive review to help students prepare for the final examination. Key topics revisited through the mock exam questions include: *Fundamentals of Optimization:* Analysis of convex sets and functions. *Gradient-Based Methods:* Discussion on Gradient Descent and its convergence properties. *Stochastic Optimization:* Insights into Stochastic Gradient Descent (SGD) and its variants used in machine learning. *Constrained Optimization:* Applying KKT conditions and understanding Lagrangian duality. *Advanced Topics:* Brief look into proximal gradient methods and momentum-based acceleration. Whether you are a student at LMU or a self-learner, this walkthrough serves as a summary of the mathematical foundations and algorithmic approaches in modern optimization for ML. *Course:* Optimization for Machine Learning (Summer 2023) *Institution:* Ludwig-Maximilians-Universität München (LMU) *Instructor:* Viktor Bengs [   • 11th Exercise, Optimization for Machine Le...  ](https://www.google.com/search?q=https...)