Machine Learning

Course on the trends and paradigms of machine learning, covering the mathematical foundations and the development of different models for their implementation in data-science projects. Group A1 · Fall 2026 (17 weeks · 34 classes · 68 hours). Prerequisites: multivariable calculus, nonlinear optimization, linear algebra, probability, and structured programming (Python).

📄 Course study guide / Guía de estudio (PDF)

Learning goals / Fines de aprendizaje

1. Provide students with a first theoretical and practical approach to the field of machine learning.
2. Analyze and understand different supervised and unsupervised learning models — their theoretical formulation, practical implementation in a programming language, and their advantages and disadvantages.
3. Introduce students to the training cycle of machine-learning models.
4. Introduce different computing tools for implementing models in Python 3.

Syllabus / Temario

1. Introducción al Machine Learning
   • ¿Qué es el Machine Learning?
   • Componentes del aprendizaje
   • Tipos de aprendizaje (supervisado, no supervisado y por refuerzo)
2. Primeros modelos de aprendizaje
   • Regresión lineal simple y múltiple
   • Gradiente descendente para optimizar parámetros
   • Métricas de evaluación de modelos de regresión
   • Regresión logística
   • Métricas de evaluación de modelos de clasificación
   • Regularización de modelos de regresión (Ridge y/o Lasso)
3. Otros modelos de aprendizaje supervisado
   • K Vecinos Más Cercanos (KNN)
   • Redes neuronales: neurona de McCulloch-Pitts, perceptrón de Rosenblatt, perceptrón multicapa, backpropagation, implementación con Keras y consideraciones prácticas de entrenamiento
4. Entrenamiento de modelos supervisados en Machine Learning
5. Modelos de aprendizaje no supervisado
   • Clústering
   • K-Means
   • Análisis de componentes principales y reducción de dimensionalidad
6. Teoría sobre Machine Learning
   • Factibilidad de aprender de datos
   • Medidas de error
   • Teoría de la generalización y sus límites
   • Intercambio entre generalización y aproximación

Coursework / Actividades

• Lectures with slide decks that synthesize the key ideas of each topic.
• Hands-on lab sessions in Google Colab (Python 3) implementing the methods.
• Readings of scientific articles on applications, paradigms, and the current landscape of machine learning.

Assessment / Evaluación

| Instrument | Weight | |—|—| | Homework, activities & computing labs | 40% | | Assessment 1 (written exam) | 20% | | Assessment 2 | 20% | | Assessment 3 (final project) | 20% | | Total | 100% |

Key dates / Fechas importantes

• First exam: October 8, 2026
• Second exam: November 10, 2026
• Final project & homework due: December 1, 2026
• Grades submitted: December 3, 2026
• Last day of classes: December 5, 2026

Suggested bibliography

Primary texts (★):

• ★ Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H.-T. (2012). Learning from Data: A Short Course. AMLBook.
• ★ Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O’Reilly.
• ★ Murphy, K. P. (2022). Probabilistic Machine Learning: An Introduction. MIT Press.
• ★ Wüthrich, M. V., & Buser, C. (2017). Data Analytics for Non-Life Insurance Pricing. SSRN Electronic Journal.

Additional references:

• James, G., et al. (2023). An Introduction to Statistical Learning: With Applications in Python. Springer.
• Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning. Springer.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
• Müller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python. O’Reilly.
• Calin, O. (2020). Deep Learning Architectures: A Mathematical Approach. Springer.
• Dixon, M. F., Halperin, I., & Bilokon, P. (2020). Machine Learning in Finance. Springer.
• López de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.