Tom (Tomáš) Pecher

Artificial Intelligence Tutorial

This course covers the vast field of Artificial Intelligence (AI). As I covered when discussing my AI experience (see AI), I consider "intelligence" to be a broad term, describing any algorithm or system in which simple rules or instructions result in some surprising, complex or desirable outcome/behaviour. For this reason, I cover all of the four main schools of AI:

• Traditional AI - for the purposes of this course, this includes anything that can be considered AI but is not ML (symbolic AI, genetic algorithms, etc.).
• Supervised Learning - the subfield of machine learning where the goal is to learn from labeled data in order to predict future outcomes.
• Unsupervised Learning - the subfield of machine learning where the goal is to analyse unlabeled data in order to understand its substructure and possibly generate new data.
• Reinforcement Learning - the subfield of machine learning where the goal is to learn from interactions with a simulated environment in order to optimise long-term goal-driven behaviour.

Disclaimer: The tutorials on this site are based on my personal study and interest. While some material reflects topics I have learned at university or from textbooks and research papers, all explanations are written in my own words and presented with my own perspective. Standard concepts belong to the wider academic community, and I acknowledge the contributions of authors and educators whose work informs my learning. Any errors or interpretations are my own.