Study materials for the Artificial Intelligence course at ФИНКИ (Faculty of Computer Science and Engineering), Skopje.
Textbook: "Artificial Intelligence: A Modern Approach" (3rd Edition) — Stuart Russell & Peter Norvig
Course page: https://www.finki.ukim.mk/mk/subject/F23L2S030
ai/
├── Planovi_Teorija_DOCX/ # Lesson notes (Word format)
├── Planovi_Teorija_PDF/ # Lesson notes (PDF format)
├── VI_2024_2025/ # FINKI exercises & search framework
│ ├── searching_framework/ # Python search framework (uninformed + informed)
│ ├── av1/ - av9/ # Exercise solutions
│ └── python_practice_exercises.md
└── README.md
Comprehensive notes for the first partial exam (6 lessons). Each file covers theory, pseudocode, worked examples, the FINKI Python framework, and fully solved exercises with code.
Available in both .docx and .pdf in their respective folders:
| # | Lesson | Topic |
|---|---|---|
| — | ai_searching_framework_guide |
Guide to the FINKI Python search framework |
| 1 | Lesson_1_Introduction |
Agents, environments, PEAS, agent types |
| 2 | Lesson_2_Uninformed_Search |
BFS, DFS, DLS, IDDFS, UCS |
| 3 | Lesson_3_Informed_Search |
Greedy search, A*, heuristics |
| 4 | Lesson_4_CSP_Constraint_Satisfaction_Problems |
CSP, backtracking, arc consistency |
| 5 | Lesson_5_Adversarial_Search |
Minimax, alpha-beta pruning |
| 6 | Lesson_6_Genetic_Algorithms |
Genetic algorithms, selection, crossover, mutation |
This folder is cloned from the official FINKI course repository:
https://github.com/fcse-intelligent-systems/ai
It contains the Python search framework and all exercise solutions used throughout the course.
To run exercises:
cd VI_2024_2025
python -m av3.explorergit clone https://github.com/YOUR_USERNAME/ai.gitOpen the PDFs to study, or the DOCX files if you want to edit/annotate.
Made for ФИНКИ students preparing for the AI partial exam. Good luck! 🍀