> ## Documentation Index > Fetch the complete documentation index at: https://aegean.ai/llms.txt > Use this file to discover all available pages before exploring further. # Introduction to Artificial Intelligence > Course introduction. Introduction to Artificial Intelligence ## What this course is all about Artificial Intelligence (AI) addresses one of the ultimate puzzles humans are trying to solve: How is it possible for a brain, whether biological or electronic, to perceive, understand, predict and manipulate a world far larger and more complicated than itself? And how do people create a machine (or computer) with those properties? To that end, AI researchers try to understand how seeing, learning, remembering and reasoning can, or should, be done. This course introduces students to the many AI concepts and techniques including perception, probabilistic reasoning over time, logical reasoning, planning with and without interactions with the environment, reinforcement learning and natural language understanding. ## Topics Covered The learning problem, entropy, optimization fundamentals, and classification basics. Fundamentals of deep learning, network architectures, forward propagation, and backpropagation. RNNs, LSTMs, transformers, and self-attention mechanisms for language understanding. Propositional logic, automated reasoning, logical inference, and intelligent agents. PDDL-based planning, logistics, and manufacturing applications. Model-based and model-free algorithms, policy gradient methods, and value-based control. *** [Edit this page on GitHub](https://github.com/aegean-ai/eaia/edit/main/src/courses/ai/index.mdx) or [file an issue](https://github.com/aegean-ai/eaia/issues/new/choose).