Artificial Intelligence

What you'll learn

The key areas this subject covers, mapped to the official curriculum.

• Search as the backbone of problem solving: BFS, DFS, A* and hill climbing
• Adversarial search and minimax for game playing
• Knowledge representation with logic and expert systems
• Machine learning paradigms: supervised, unsupervised and neural networks
• Applications from NLP and vision to robotics and AI ethics

Course units

The structured path through this subject.

1. 1

   Introduction

   What artificial intelligence is, its history, and how to judge whether a machine is intelligent.

   4 topics · 4 lessons available

2. 2

   Intelligent Agents

   Agents that perceive, decide and act, and the environments they live in.

   4 topics · 4 lessons available

3. 3

   Problem Solving by Searching

   How agents solve problems by exploring state spaces with uninformed, informed and local search.

   6 topics · 6 lessons available

4. 4

   Knowledge Representation

   How to represent what an agent knows, and how to reason with that knowledge.

   5 topics · 5 lessons available

5. 5

   Machine Learning

   Programs that improve with data: paradigms, supervised models, clustering and neural networks.

   5 topics · 5 lessons available

6. 6

   Applications of AI

   Where AI meets the real world: language, vision, robotics, and the ethics that come with it.

   4 topics · 4 lessons available