Curriculum 2025.1 – Starting in 2025
Curriculum Structure
Courses and Training Groups – PPGMMQ
The courses are divided into 5 groups.
Courses in Groups A, B, and C aim to expand or reinforce the students’ foundational training and standardize the level of incoming students. Courses in Groups D and E are designed to provide specific training, more focused on the dissertation or thesis work.
Requirement: Master’s (resp. Doctoral) students must complete at least three (resp. four) courses from Groups A, B, and C, with at least one from each group, chosen in agreement with the advisor.
Introduction to Modeling
Computational Intelligence
Statistical Inference
Probability
Graph Algorithms
Linear Optimization
Applied Linear Algebra
Longitudinal Data Analysis
Complex Network Analysis
Machine Learning
Database Systems
Polyhedral Combinatorics
Heuristics and Metaheuristics
Bayesian Inference
Digital Signal Processing Laboratory
Computational Methods in Statistics
Multivariate Modeling Methods
Regression Models
Combinatorial Optimization
Integer Optimization
Multiobjective Optimization
Non-Linear Optimization
Stochastic Processes
Scientific Programming
Advanced Scientific Programming
Stochastic Dynamic Programming
Neural Networks and Deep Learning
Game Theory and Conflict Analysis
Special Topics in Modeling and Quantitative Methods I
Special Topics in Modeling and Quantitative Methods II
Advanced Topics in Modeling and Quantitative Methods I
Advanced Topics in Modeling and Quantitative Methods II
Courses from other Graduate Programs
To view the syllabus of the courses offered by MMQ, please visit:
