* Class 1	2025-02-17
   Data-driven decision making: introduction to analytics.

  * Class 2	2025-02-17
   Introduction to optimization: using software (labs).

  * Class 3	2025-02-24
   Linear optimization.
   Optimization: duality, economic interpretation. Examples.

  * Class 4	2025-02-24
   Labs: resolution of worksheets numbers 1 and 2.

  * Class 5	2025-03-10
   Integer optimization.  
   Capacitated facility location problems.
   Uncapacitated facility location problems.

  * Class 6	2025-03-10
   Labs: resolution of worksheet number 3.

  * Class 7	2025-03-17 *
   Capacitated facility location problems.
   Optimization in graphs: the coloring problem.

  * Class 8	2025-03-17 *
   Labs: resolution of worksheet number 4.

  * Class 9	2025-03-24
   Q&A: assignment 1.

  * Class 10	2025-03-24
   Labs: assignment 1.

  * Class 11	2025-03-31
   Optimization in graphs: partitioning, maximum stable set and maximum clique.
   
  * Class 12	2025-03-31
   Labs: resolution of worksheet number 5.

  * Class 13	2025-04-07
   Optimization: Matching problems.
   
  * Class 14	2025-04-07
   Labs: resolution of worksheet number 6.
   
  * Class 15	2025-04-28 *
   Q&A: assignment 2.
   
  * Class 16	2025-04-28 *
   Labs: assignment 2.
   
  * Class 17	2025-05-12
   Case study: the kidney exchange problem.

  * Class 18	2025-05-12
   Labs: resolution of worksheet number 7.

  * Class 19	2025-05-19
   Introduction to machine Learning: Introduction, notation.
    
  * Class 20	2025-05-19
   Labs: resolution of worksheet number 8.
   
  * Class 21	2025-05-26
   Introduction to machine learning: Examples with Python and Scikit-learn.

  * Class 22	2025-05-26
   Labs: resolution of worksheet number 8 (retake).
    
  * Class 23	2025-06-02 *
   Q&A: assignment 3.
   
  * Class 24	2025-06-02 *
   Labs: assignment 3.