Temas Selectos de Sistemas de Información

Elective course bridging operations research and deep learning: it covers decision-making with mathematical, statistical, and probabilistic tools, linear optimization, and an introduction to deep neural networks and their current applications. Group A · 6-week intensive term (May 25 – July 3, 2026) · 60 total hours. Prerequisites: linear algebra, multivariable calculus, and structured programming (Python).

📄 Course study guide / Guía de estudio (PDF)

Learning goals / Fines de aprendizaje

1. Solve decision-making problems using mathematical, statistical, and probabilistic tools.
2. Introduce students to problem modeling with linear functions, the field of linear optimization, and methods for solving such problems.
3. Introduce students to deep neural networks — their fundamentals, variants, and applications to problems of current interest.

Syllabus / Temario

1. Investigación de Operaciones
   • Introducción a la Investigación de Operaciones
   • Introducción a la Programación Lineal
   • Solución de problemas de Programación Lineal: Método Simplex
2. Redes Neuronales (profundas)
   • Introducción al aprendizaje automático
   • Introducción a redes neuronales profundas
   • Entrenamiento de redes neuronales
   • Redes Neuronales Convolucionales (CNN)
   • Redes Neuronales Recurrentes (RNN)
   • Autoencoders y Variational Autoencoders
   • Generative Adversarial Networks (GAN)

Coursework / Actividades

• Lectures on each topic by the instructor.
• Exercises and activities practicing the linear-programming methods seen in class.
• Programming exercises and computing-lab sessions for the hands-on topics.
• Readings of scientific articles on applications, paradigms, and philosophy of the topics covered.
• Five homework assignments on the operations-research part.
• A final project on the deep-neural-networks part.

Assessment / Evaluación

| Instrument | Weight | |—|—| | Homework | 50% | | Computing labs | 25% | | Final project (neural networks) | 25% | | Total | 100% |

Key dates / Fechas importantes

• First day of classes: May 25, 2026
• Final project due: July 1, 2026
• Grades submitted: July 2, 2026
• Last day of classes: July 3, 2026

Suggested bibliography

• Bazaraa, M. S. (1998). Programación lineal y flujo en redes. Limusa.
• Gass, S. I. (1975). Linear Programming: Methods and Applications. McGraw-Hill.
• Hillier, F. S. (2010). Investigación de operaciones. McGraw-Hill Interamericana.
• Papadimitriou, C. H., & Steiglitz, K. (1998). Combinatorial Optimization: Algorithms and Complexity. Dover.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
• Foster, D. (2019). Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play. O’Reilly.
• Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O’Reilly.