- Lunes 8/01: 1-2 Introducción (Prof. Maikol Solís)
- Miércoles 10/01: 3. Fundamentals Algoritms
- 3.1 Linear Regresion (Hernán)
- 3.2 Logistic regression (Andrey)
- 3.3 Decision Tree Learning (José David)
- 3.4 Support Vector Machine (Daniel)
- 3.5 k-Nearest Neighbors (Daniela)
- Lunes 15/01: 4. Anatomy of a Learning Algorithm. (Gianluca Lo importante es explicar el algoritmo de Gradient Descend)
- Miércoles 17/01: 5. Basic Practice
- 5.1 Feature Engineering (Roberto)
- 5.2 Learning Algorithm Selection y 5.3 Three Sets (Martha)
- 5.4 Underfitting and Overfitting y 5.5 Regularization (Francisco)
- 5.6 Model performance assesment (Hernán)
- 5.7 Hyperparameter Tunning (Andrey)
- Lunes 22/01: 6.1 Neural Networks (José David)
- Miércoles 24/01: 6.2 Deep Learning (Daniel)
- Lunes 29/01: 7. Problems and solutions
- 7.1 Kernel Regression (Daniela)
- 7.2 Multiclass Clasificaton (Gianluca)
- 7.3 One-Class Clasificaton (Roberto)
- Miércoles 31/01: 7. Problems and solutions
- 7.4 Multi-label Classification (Martha)
- 7.5.1 Random Forest (Francisco)
- 7.5.2 Gradient Boosting (Hernán)
- Lunes 5/02: 8. Advanced practice (Prof. Maikol Solís)
- Miércoles 7/02: 9. Unsupervised Learning.
- 9.1 Density estimation (Andrey)
- 9.2.1 k-Means (Daniela)
- 9.2.2 DBSCAN and HDBSCAN (Gianluca)
- 9.2.3 Determining the number of Clusters (Roberto)
- 9.2.4 Other Clustering Algoritms (Martha)
- 9.3.1 Principal Component Analysis (Francisco)
- 9.3.2 UMAP (Prof. Maikol Solís)