We start our journey into artificial intelligence (AI) with key definitions and concepts, applications, and history of AI.

In this unit, we investigate fundamental intelligent agents starting with rational agents.

This lesson covers logical agents, also known as symbolic agents or knowledge-based agents. Logical agents are useful encoding for domain-specific knowledge.

Search agents are agents that work towards a goal. The agent’s job is to identify the action or series of actions that lead to the goal. There are different types of search: (1) simple search, (2) search with constraints, and (3) adversarial search.

In this unit, we dive deep into machine learning (ML). Supervised learning algorithms (e.g., decision trees) learn from labeled data to make predictions whereas unsupervised learning algorithms (e.g., K-means) discover hidden patterns in unlabeled data.

Neural networks can learn to recognize patterns, make decisions, and even generate new content. From image recognition in social media apps to voice assistants on our phones, neural networks are behind many of the AI technologies we use every day.

In this unit, we'll take a look at large language models (LLMs) and the code behind OpenAI's ChatGPT.

In this unit, we discuss the broader impact of AI on society. We cover AI ethics which is concerned over the responsible use of AI and its consequences and future potential of AI across various fields. Finally, we introduce large language models (LLMs) as a bonus section.