Boxi YuEmail

Courses · interactive lecture notes and videos

Every lecture you can run.

Companion sites for six courses, from AI literacy for everyone to building software with coding agents. Each lecture lets you step through what the code or the agent actually does, asks you to predict before it shows you, and ends with a self-check.

Agentic SE open course ↓
  1. 1
    Step throughWatch each idea unfold one step at a time, from code to AI answers.
  2. 2
    PredictCommit to an answer before the page shows you what really happens.
  3. 3
    CheckEnd every lecture with a self-check and exercises.
Open course · No coding8 modules

AI Literacy for Everyone

Open course in Chinese and English · Designer: Boxi Yu · 中文版

Eight shared modules on using AI, checking what it says, and knowing when not to use it, with tracks for K12, university, working adults, seniors, and teachers. The core module, C4, teaches how to check AI: it makes mistakes, so learn to catch them.

FromC1What AI is and isn'ttoC8When not to use AI

Notes, 3–5 minute videos, and interactive pages per moduleOpen the notes
Introductory · Python11 lectures

Introduction to AI Programming

CUHK–Shenzhen · Instructor: Prof. Pinjia He

From number systems to recursion, classes, and data structures, ending with how large language models work. Step through what Python does in memory, predict each output before it appears, and finish every lecture with a self-check.

From01Computers and Number Systemsto11Large Language Models

Notes and 3–4 short videos per lectureOpen the notes
Core · Python & Java9 chapters

Data Structures

Python and Java versions · Instructors: Boxi Yu and Yintong Huo

The structures behind every program, from arrays and linked lists to search trees, hash tables, heaps, and graphs, with complexity and proof methods as the common language. Pick Python or Java: the concepts are shared, the code is in your language.

From01Complexity and Proof Methodsto09Graphs and Graph Algorithms

Notes and four 8–10 minute videos in every chapterOpen the notes
Core · Math with code30 lectures

Linear Algebra

Open course in English · Designer: Boxi Yu · Inspired by MIT 18.06

From three pictures of a linear system to the singular value decomposition and computing with matrices. Every number in the notes is computed by running code, and every unit ends with a machine-learning example and a section on working with AI: what to ask, and how to check the answer.

FromL01–05Equations and eliminationtoL29–30Computing with matrices

Notes, demos, and two short videos per lecture, plus electivesOpen the notes
Core · Neural networks3 lectures

Machine Learning

CUHK–Shenzhen · Lectures 9–11 · Instructor: Prof. Jicong Fan

From a single neuron to attention: backpropagation on computational graphs, convolution and pooling, recurrent networks with LSTM and GRU, and the Transformer. Move the numbers, predict each result, and run the NumPy labs.

From09Neural Networksto11RNN and Transformer

Notes, nine videos with English and Chinese subtitles, and NumPy labsOpen the notes
Advanced · Coding agents11 lectures

AI-assisted Software Engineering

CUHK–Shenzhen · Fall 2026 · Instructor: Jinsheng Ba · Official course site

How LLMs and agents work, then how to use them across the software lifecycle: prompts, MCP and skills, requirements, coding, and team collaboration. Step through the agent loop, prompts, and tool calls, then try each step with a coding agent of your own.

From00Introductionto10Team Collaboration

Notes and videos, released with the lecturesOpen the notes

Interactive notes and videos built with 培玉智能. I designed AI for All and LA101, teach Data Structures with Yintong Huo, and co-designed the AIE1001, DDA3020 and CSC4801 notes; their slides and course content belong to each instructor.

Open course · Fall 2026

Agentic Software Engineering

A practical twelve-week course on designing, evaluating, and shipping software with coding agents. It connects prompts and tools to the engineering disciplines that keep systems dependable.

For
Software engineers, researchers, and advanced students
Topics
Agent loops, tool use, test design, evaluation, and reliability
Format
Notes, worked examples, and small experiments you can reproduce
Browse the course materials ↗

Teaching & supervision

Classroom teaching, PhD supervision, and mentoring.

Courses I taught at CUHK–Shenzhen, and where the students I mentored went next.

Teaching & supervision →