Basic Machine Learning for Scientists

2021. I was a teaching assistant in the first edition of this 7-week graduate course. I designed and implemented, with the other TA, the notebooks for the practical classes (link to notebooks), that include the course evaluations, and we led those classes. Course website

The course covers basic concepts of machine learning, to give an overview of the area, while also providing students with hands-on experience and exercises of varying difficulty levels.

The notebooks, which are in Spanish, can be found in my GitHub repository. The list of notebooks is as follows:

• Class 1: Introduction to ML
• Class 2: Supervised learning
• Class 3: Unsupervised learning
• Class 4: Representing the data
• Class 5: Neural networks
• Class 6: Model evaluation
• Class 7: Applications, spike sorting