Launched in 2025 (as SUSTech-Kai-Notes), Kai Course Notes is a personal initiative motivated by Cambridge Notes. It collects the course notes and materials from my studies at SUSTech, UC Berkeley, the University of Oxford, and elsewhere.
🏠 Landing page: kai-course-notes.kaichen.dev — the course catalog at a glance
📦 Downloads: grab any course as a single zip from the downloads release
📝 Web-native notes (short conceptual write-ups, best read in the browser) live on the Notion branch: kai-course-notes.notion.site
🌐 Main personal website: kaichen.dev
Repository layout: <University>/<Course>/… — each course folder has its own README.
| Course | Term | Download |
|---|---|---|
| MA215 Probability Theory | Fall 2024 | zip |
| MA217 Mathematical Experiments | Fall 2024 | zip |
| MA219 Abstract Algebra (H) | Fall 2024 | zip |
| MA230 Ordinary Differential Equations A (H) | Spring 2025 | zip |
| MA231 Mathematical Analysis III (H) | Fall 2024 | zip |
| MA232 Complex Analysis (H) | Spring 2025 | zip |
| MA234 Introduction to Big Data Analysis | Spring 2025 | zip |
| MA305 Numerical Analysis | Fall 2025 | zip |
| MA336 Partial Differential Equations (H) | Fall 2025 | zip |
| MA337 Real Analysis (H) | Fall 2025 | zip |
| MA340 Fourier Analysis Seminar | Spring 2025 | zip |
| MAT7092 Stochastic Processes | Fall 2025 | zip |
| Course | Term | Download |
|---|---|---|
| CS108 Introduction to Mathematical Logic (H) | Spring 2024 | zip |
| CS109 Introduction to Computer Programming | Fall 2024 | zip |
| CS201 Discrete Mathematics | Spring 2025 | zip |
| CS217 Data Structures and Algorithm Analysis (H) | Fall 2025 | zip |
| Course | Term | Download |
|---|---|---|
| STA203 Foundation of Probability Theory | Fall 2024 | zip |
| MA204 Mathematical Statistics | Spring 2025 | zip |
| Course | Term | Download |
|---|---|---|
| COMPSCI 61A The Structure and Interpretation of Computer Programs | Spring 2026 | zip |
| COMPSCI 61BL Data Structures and Programming Methodology | Summer 2026 | zip |
| COMPSCI 188 Introduction to Artificial Intelligence | Summer 2026 | zip |
| Course | Term | Download |
|---|---|---|
| Deep Unsupervised Learning | Summer 2025 | zip |
| Course | Term | Download |
|---|---|---|
| YMSC Introduction to Data Assimilation (Tsinghua YMSC) | Fall 2025 | zip |
Everything I authored — handwritten & LaTeX notes (with sources), homework write-ups, lab reports, and project code — is shared throughout under the license below. Materials provided by instructors and institutions are handled according to each school's own rules:
- SUSTech — the university publishes no explicit policy on redistributing course materials, and general copyright remains with the lecturers. Lecturer-provided slides and handouts are shared here in good faith under a strict takedown promise: any lecturer may request removal at any time, and per the license note below, all downloads of removed material — including those made before the request — become unauthorized.
- UC Berkeley — course policy prohibits publicly posting solutions to homework, labs, and projects (assignments are reused across semesters), and staff-authored materials are not redistributed. Only my own practice code and notes appear here; official materials live on the course websites.
- University of Oxford — under my programme's enrolment agreement, all college-provided materials remain the college's intellectual property and are not redistributed. Only my own project results appear here.
- All schools — no published textbooks (each course README lists the reference books instead; please obtain them through proper channels), and no homework/exam solutions or OJ reference programs, ever.
For instructors and rights holders: if you would prefer that any of your material not appear here, I sincerely apologize — please email me directly at kaichen.dev.37@gmail.com, or open a takedown request. Removal is honored promptly, and per the license note below, prior downloads of removed material become unauthorized.
Without specific clarification, all original content within this repository is protected under the CC BY-NC-SA 4.0 license.
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
- Attribution — give appropriate credit and indicate if changes were made
- NonCommercial — may not be used for commercial purposes
- ShareAlike — distribute contributions under the same license
Some resources retain their rights reserved by the respective lecturers. Once a lecturer requests removal of their materials, all downloads — including those made prior to the request — are no longer authorized.
Please also read the Code of Conduct before using any materials in this repository.