10-202: Introduction to Modern AI by Zico Kolter

This course provides an introduction to how modern AI systems work. By “modern AI”, we specifically mean the machine learning methods and large language models (LLMs) behind systems like ChatGPT, Gemini, and Claude. [Note] Despite their seemingly amazing generality, the basic techniques that underlie these AI models are surprisingly simple: a minimal LLM implementation leverages a fairly small set of machine learning methods and architectures, and can be written in a few hundred lines of code.

This course will guide you through the basic methods that will let you implement a basic AI chatbot. You will learn the basics of supervised machine learning, large language models, and post-training. By the end of the course you will be able to write the code that runs an open source LLM from scratch, as well as train these models based upon a corpus of data. The material we cover will include:
• A brief history of AI
• Supervised machine learning
• Linear models
• Loss functions and optimization
• Neural networks
• Large language models
• Self attention and transformers
• Tokenizers
• Efficient inference
• Post-training
• Supervised fine tuning
• Alignment and instruction tuning
• Reasoning models and reinforcement learning
• Safety and security of AI systems

The topics above are a general framing of what the course will cover. However, as this course is being offered for the first time in Spring 2026, some elements are likely to change over the first offering.

Links:
• Course
• YouTube

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