A lot of stuff.

README.md

@@ -0,0 +1,18 @@
+# Applied Optimization and Machine Learning
+
+This repo contains all the materials I used while giving the course Applied
+Optimization and Machine Learning techniques during year 2021/2022.
+
+It contains the following materials:
+
+- Lecture notes: the different notes I used in the classes.
+- Cases: the three cases that students had to do during the course. Each case
+  includes a narrative description, data and some helping code. The folders
+  also contain the grading details.
+- Exam: the exam, along with the grading helper to calculate the final scores
+  of students.
+- Other: a bit of everything.
+  - Random notes
+  - Memories I want to keep.
+  - Interesting materials that I did not directly use during the course.
+  - Ideas for next year.

cases/case_3/case_3_description.md

@@ -128,8 +128,7 @@ A few comments on your notebook:
     - [A guide on regression performance metrics](https://machinelearningmastery.com/regression-metrics-for-machine-learning/)
       and
       some [material from scikit-learn on the same topic](https://scikit-learn.org/stable/modules/classes.html#regression-metrics)
-    -
-  An [introduction to cross-validation](https://machinelearningmastery.com/k-fold-cross-validation/)
+    - An [introduction to cross-validation](https://machinelearningmastery.com/k-fold-cross-validation/)
     - A
       thorough [review on why we need to use baselines](https://blog.ml.cmu.edu/2020/08/31/3-baselines/)
       in ML

logbook.md

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-## Lecture 1 - 7/4
-
-- Upload materials
-- Allow groups of 4
-- Find out how to split seminar groups
-- Upload Python prep materials
-
-After class:
-- Fix the syllabus and tell Sira about it.
-- Sign contract again and set it over to Emili again.

other/ideas.md

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+# Ideas for next year
+
+- Using Kaggle for the competition and making it individual. This would
+  definetely make the logistics easier, and the individual part of it would
+  probably be much more stimulating. It would also help me better spot who is
+  really interested in the course.
+- I am thinking about switching from the 3-part approach to the course (
+  Simulation-Optimization-ML) to a two part (Optimization and ML). The contents
+  would be roughly the same, but the simulation part would be more geared
+  towards the serious metaheuristics and we would spend a bit less time in the
+  mathematical programming bit. I just realized that, in my mind, I had
+  packaged simulation as something completely different from optimization. But
+  that is quite silly. Optimization is optimization, no matter if you are doing
+  it through Linear Programming or through metaheuristics. The role of
+  simulation is to compute the value of a target function in a heuristic
+  environment. That's it. Hence, the simulation case then would not be so
+  naive, but rather require students to build a genetic algorithm solution to
+  tackle a more complex case of optimization, such as a multiechelon inventory
+  optimization situation.
+- I have also changed my mind on the course grading. I really don't like to do
+  the exam. The cases have so much more value when compared to the exam. Some
+  of the students let me know that they thought that grading was not balanced
+  with the exam having so much weight, and I can't help but agree. I will
+  probably try to change the weight, but I still need to find a way to prevent
+  free-loaders from going through the course unharmed. I have thought that
+  maybe I could make the students work in groups of 2. The chance of having
+  free-loaders lowers by a lot. And then I could pull the cases grade up to 75%
+  and the exam would only by 25%.
+- For next year, I want to include a few additional ideas in the first welcome
+  class:
+    - Publish the grades from Avaldo and show them to the new students.
+    - Give a lessons learnt from last year to students (what did successful
+      students do, what did students that failed do)
+    - Show places where students from last year are today (Iker, Ivan, Marc, etc)

other/memories.md

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+- Carla preguntando en la última clase como había acabado en la UPF.
+- Iker y la nota que le faltaba para poder echar su application en Cambridge.
+- Vicent y Arnau pidiéndome si podiamos quedar porque querían orientación de cómo entrar en el mundo Data Science.
+- El empujoncito que le di a Iván para sacar la matrícula.
+- El culebrón entre Jennifer, Marta y Anar al final del curso.
+- La cara de alegría de Álvaro cuando les dí el libro de premio por la competición.
+- Los gestos de sísísí de Marta.
+- El aplauso que me dieron en mi discurso de despedida en la última clase.

random_notes.md

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-Classroom for lectures -> 20.047
-Classroom for seminars -> 40.501
-Despachos asociados -> 40.171 i 40.173
-
-# Before starting doubts
-
-- How do I build the course plan? How do I upload it?
-- What should I expect from Sira?
-- Office hours?
-- Metodos para evaluarme a mi?
-- Sobre las clases
-  - Portatil propio o usar el PC del aula?
-  - Microfono?
-  - Grabar?
-  - Mascarilla?
-
-Case 1
-Python colab resources
-Primer deck