KAUST

• DSA 008, Introduction to Machine Learning (Fall 2025)
• CS 332, Federated Learning (Fall 2025)
• CS 331, Stochastic Gradient Descent Methods (Fall 2025)
• CS 398, CS Graduate Seminar (Fall 2025)
• DSA 211, Introduction to Optimization (Summer 2025)
• CS 331, Stochastic Gradient Descent Methods (Spring 2025)
• CS 331, Stochastic Proximal Point Methods (Spring 2024)
• DSA 006, Introduction to Machine Learning (Summer 2023)
• CS 332, Federated Learning (Spring 2023)
• DSA 008, Introduction to Optimization (Fall 2022)
• CS 331, Stochastic Gradient Descent Methods (Fall 2022)
• CS 332, Federated Learning (Spring 2022 - Piazza)
• CS 331, Stochastic Gradient Descent Methods (Fall 2021 - Piazza)
• CS 332, Federated Learning (Spring 2021 - Piazza)
• CS 331, Stochastic Gradient Descent Methods (Fall 2020 - Piazza)
• CS 390T, Special Topics in Federated Learning (Spring 2020 - Piazza)
• CS 394D, Contemporary Topics in Machine Learning (Spring 2018, Spring 2019 - Piazza)
• CS 390FF, Big Data Optimization (Fall 2017, Fall 2018, Fall 2019 - Piazza)

University of Edinburgh

• Modern Optimization Methods for Big Data Problems (Spring 2016, Spring 2017)
• Game Theory (Fall 2010, 2011, 2012)
  
• Discrete Programming and Game Theory (Fall 2010, 2011, 2012)
• Optimization Methods in Finance (Spring 2012, 2013, 2014, 2015)
• Deterministic Optimization Methods in Finance (Spring 2011, 2012, 2013, 2014, 2015)

External (e.g., Summer / Winter Doctoral Schools)

• 1-DAV-213: Introduction to Stochastic Gradient Descent Methods (5 x 4.5 hours)
  School of Mathematics, Physics and Informatics (Comenius University), Slovakia, June 20-24, 2022
  If you are from FMFI UK, register for the course via Piazza using your fmph.uniba.sk email address. Otherwise, I will register you manually once you send an email to me.
• 390015 KU VGSCO: Stochastic Gradient Descent Methods (2022S), University of Vienna, Austria, May 30-June 3, 2022
• A Guided Walk Through the ZOO of Stochastic Gradient Descent Methods (6 hours)
  Event: ICCOPT 2019 Summer School, Berlin, Germany, August 2019
  main slides, extra slides on SGD-SR and SEGA
• Randomized Optimization Methods (5 hours)
  Event: Data Science Summer School, CMAP, École Polytechnique, Paris, France, August 2017
  slides, lecture videos: 1 2 3 4 5
• Randomized Algorithms for Big Data Optimization (21 hours)
  Event: Graduate School in Systems, Optimization, Control and Networks (SOCN), Université catholique de Louvain, Belgium, Fall 2015
• Optimization in Machine Learning
  Event: Machine Learning Thematic Trimester, Toulouse, France, September 2015
• Randomized Coordinate Descent for Big Data Optimization (6 hours)
  Event: Khronos-Persyval Days on High-Dimensional Learning and Optimization, Grenoble, France, June 2014