A collection of programming projects exploring fundamental artificial intelligence algorithms, computational problem-solving, and intelligent decision-making.
The repository focuses on applying theoretical AI concepts to practical computational problems, including search strategies, optimization, reasoning under uncertainty, and machine learning.
The projects explore three main areas of artificial intelligence:
- Search and Optimization: Exploring state spaces, evaluating possible solutions, and applying search strategies to complex problems.
- Probabilistic Reasoning and Decision-Making: Modeling uncertainty and understanding how intelligent systems evaluate alternative actions.
- Machine Learning: Exploring learning algorithms and methods for making predictions and improving decisions from data or experience.
- Uninformed and heuristic search
- State-space exploration
- Local optimization
- Constraint satisfaction
- Adversarial search and game-based decision-making
- Probability-based inference
- Bayesian networks
- Utility-based decision-making
- Decision networks
- Markov decision processes
- Naive Bayes classification
- Perceptron learning
- Instance-based learning
- Kernel methods
- Reinforcement learning
This repository contains five programming projects covering selected topics in artificial intelligence.
Each project focuses on implementing or experimenting with methods for solving computational problems.
Algorithmic Problem-Solving
Representing problems computationally and exploring different methods for finding solutions.
Intelligent Decision-Making
Applying mathematical and algorithmic approaches to evaluate possible actions, outcomes, and uncertainty.
Machine Learning Fundamentals
Exploring how computational models can identify patterns, learn from examples, and support predictions or decisions.
Computational Reasoning
Connecting mathematical foundations with practical programming implementations.
These projects were developed as part of my studies in Computer Science and Engineering at the University of Oulu, with a focus on artificial intelligence.
The collection documents practical work in AI-related programming and algorithmic problem-solving.