https://github.com/tornikeo/lut-principles-of-technical-computing
Solutions to LUT course "Principles of Technical Computing" in MatLab (2024)
https://github.com/tornikeo/lut-principles-of-technical-computing
lut-university matlab ode-solver solutions
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Solutions to LUT course "Principles of Technical Computing" in MatLab (2024)
- Host: GitHub
- URL: https://github.com/tornikeo/lut-principles-of-technical-computing
- Owner: tornikeo
- Created: 2023-10-05T16:21:38.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-08-08T07:17:36.000Z (11 months ago)
- Last Synced: 2025-08-08T09:17:04.837Z (11 months ago)
- Topics: lut-university, matlab, ode-solver, solutions
- Language: MATLAB
- Homepage: https://moodle.lut.fi/course/search.php?areaids=core_course-course&q=principles+of+technical+computing
- Size: 14.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
Principles of Technical Computing
Solutions to LUT University's Principles of Technical Computing course
MATLAB assignments, optimization, ODEs, statistics, and more
Contents •
Description •
Course Materials
---
## 📚 Contents
This repository contains weekly assignments for the Principles of Technical Computing course at LUT University. Each week includes a PDF describing the tasks and MATLAB `.m` files with solutions.
- [Week 1](./week1/): Image Compression with SVD
- [Week 2](./week2/): Solving ODEs in MATLAB
- [Week 3](./week3/): Parameter Optimization
- [Week 4](./week4/): Nonlinear Parameter Optimization
- [Week 5](./week5/): Parameter Sensitivity & Statistics
---
## 📝 Description
**Teaching Language:** English
**Teacher(s) in Charge:** D.Sc. (Tech.) Matylda Jablonska-Sabuka
### Aims
- Gain fluency in MATLAB syntax and programming
- Understand principles of technical computing
- Apply skills to mathematical and engineering problems
- Skills are also applicable to Octave and R
### Topics Covered
- Data structures (multidimensional arrays, cell arrays, etc.)
- Variable types (numeric, logical, textual, etc.)
- Symbolic computation
- Conditional statements (`if-else`, `switch-case`)
- Loops (`for`, `while`)
- Built-in functions
- Handling external data
- 2D/3D plotting
- User-defined functions
- Code optimization (speed, style, efficiency)
### Teaching Methods
- Lectures: 12 h
- Computer class exercises: 24 h
- Independent study: 30 h
- Exam preparation: 34 h
- **Total:** 100 h
### Assessment
- Grading: 0–5
- Examination: 100%
- Exam in Exam system: **Yes**
- Moodle/Exam schedule: **No**
### Prerequisites
- Basic university calculus (including matrix calculus)
### Places
- Exchange students: max 10
- Open University students: max 5
---
## 📖 Course Materials
- Lecture material (available in Moodle)
- Based partly on: Gilat, A.: An Introduction to Matlab with Applications
---
For questions or suggestions, feel free to open an issue or pull request!