Welcome to the repository for my Master's studies in Mathematical Engineering and Computer Science at the International University of La Rioja, Spain. This repository serves as a comprehensive collection of my notes, class materials, presentations, and activities from all four terms of the program.
Course 1:Applied Numerical Methods I
- Fundamental numerical methods for engineering and science. Includes:
- Assignments: Problem sets and group activities (e.g., articulated arm, interpolation, ODEs).
- Final Exam: Final assessment and solutions.
- Lectures: Complete lecture notes by topic.
- Matlab code: Scripts for differentiation, ODEs, integration, interpolation, and more.
- Fundamental numerical methods for engineering and science. Includes:
Course 2:High Performance Computing
- Scientific programming, parallel and distributed computing, and computational efficiency. Includes:
- Assignments: Binary tree, group activities, and more.
- Laboratory: Jupyter notebooks for NLP and concurrency.
- Lectures: Thematic notebooks and data structures.
- Notebooks: Supplementary lesson notebooks.
- Scientific programming, parallel and distributed computing, and computational efficiency. Includes:
Course 3:Python Course
- Practical Python programming for data analysis and scientific computing. Includes:
- Activity 1: Jupyter notebook for introductory Python tasks.
- Activity 2: Jupyter notebook for intermediate exercises.
- Activity 3: Jupyter notebook for advanced exercises.
- Activity 4: Jupyter notebook and Titanic dataset for applied data analysis.
- Practical Python programming for data analysis and scientific computing. Includes:
Course 1:Applied Numerical Methods II
- Advanced numerical methods for solving boundary value problems (BVPs) and partial differential equations (PDEs). Includes:
- Assignments: Group tasks and problem sets on finite differences and shooting methods.
- Final Exam: Exam solutions covering BVPs, PDEs (elliptic, hyperbolic, parabolic), and numerical methods.
- Lectures: Lesson materials organized by topic (Lessons 2-10).
- Lessons: Supplementary lesson PDFs and resources.
- Matlab code: BVP implementations (shooting & finite difference, linear/nonlinear by boundary type), IVP solvers (Adams-Bashforth, Heun), and PDE solvers (elliptic, hyperbolic, parabolic).
- Advanced numerical methods for solving boundary value problems (BVPs) and partial differential equations (PDEs). Includes:
Course 2:Stochastic Differential Equations
- Theory and applications of stochastic differential equations and stochastic processes. Includes:
- Assignments: Group tasks and problem sets with solutions.
- Final Exam: Exam materials and preparation resources.
- Lectures: Lecture notes, presentations, and whiteboard materials.
- Lessons: Learning resources and supplementary materials.
- Theory and applications of stochastic differential equations and stochastic processes. Includes:
Course 3:Derivatives and Portfolios in Finance
- Financial derivatives, portfolio optimization, and quantitative finance. Includes:
- Assignments: Group tasks, practice exercises, and Python implementations with real financial data (GOOG, IBM, NVDA).
- Final Exam: Exam materials and exam preparation resources.
- Lectures: Topic-specific PDF materials and Python code notebooks for financial analysis.
- Lessons: Learning materials organized by theme.
- Financial derivatives, portfolio optimization, and quantitative finance. Includes:
Course 1:Dynamic System Modeling
- System dynamics, simulation, and modeling techniques using MATLAB and Simulink. Includes:
- Assignments: Group tasks and problem sets.
- Final Exam: Exam materials, solutions, and Simulink models.
- Lectures: Presentation slides and topic-specific materials (Topics 1-10).
- Lessons: Lesson PDFs and supplementary resources.
- System dynamics, simulation, and modeling techniques using MATLAB and Simulink. Includes:
Course 2:Multivariable Techniques and Machine Learning
- Multivariate statistical analysis and machine learning applications in engineering. Includes:
- Assignments: Group tasks, laboratory activities with regression problems.
- Final Exam: Exam notebook and preparation materials.
- Lectures: Presentation slides and organized lesson materials (Topics 1-10).
- Lessons: Lesson PDFs and exam review guides.
- Multivariate statistical analysis and machine learning applications in engineering. Includes:
Course 3:Optimization
- Optimization algorithms, linear and nonlinear programming, and advanced optimization methods. Includes:
- Assignments: Comprehensive activities on 1D optimization, unconstrained optimization, and the Traveling Salesman Problem.
- Final Exam: Exam notebook, manual solutions, and MATLAB implementations.
- Lectures: Complete lecture materials organized by topic (Topics 1-10), covering Simplex, genetic algorithms, and particle swarm optimization.
- Lessons: Lesson PDFs and supplementary optimization resources.
- Optimization algorithms, linear and nonlinear programming, and advanced optimization methods. Includes:
- Master's Thesis:DQ-LLM-MD
- Research thesis on Data Quality for Large Language Models in Mathematical Discovery.
Feel free to explore the repository to access my academic journey throughout of the Master's program. Each course, presentation, and activity folder contains the necessary materials. Additionally, you can use the provided links to navigate to specific content.
If you have any questions or would like to connect, please feel free to reach out:
- LinkedIn:My LinkedIn Profile
Thank you for visiting my Master's repository! 📚🎓