ML Terrain Generation

My undergraduate thesis at Charles University explored how machine learning can improve procedural terrain generation. The goal was creating realistic landscapes algorithmically rather than by hand.

The research compared traditional noise-based approaches (Perlin noise, diamond-square) with ML-based methods, evaluating visual quality, computational cost, and controllability.

I graduated with First-Class Honours, and the thesis gave me a solid foundation in both practical ML implementation and academic research methodology.