I teach a AI course designed for a general audience, suitable for students from all majors.
Description
The course will gently introduce foundational AI topics.
This course takes place in HGX208 every Wednesday afternoon (13:30-16:10) during the second semester of the 2025-2026 academic year.
Schedule
| Week | Date | Topic |
|---|---|---|
| 1 | 3/4/2026 | AI Overview |
| 2 | 3/11/2026 | Machine Learning Basics |
| 3 | 3/18/2026 | Traditional AI Models |
| 4 | 3/25/2026 | Model Evaluation and Selection |
| 5 | 4/1/2026 | Artificial Neural Networks |
| 6 | 4/8/2026 | Computer Vision |
| 7 | 4/15/2026 | Course Practice and Programming |
| 8 | 4/22/2026 | Natural Language Processing |
| 9 | 4/29/2026 | RNN and LSTM |
| 10 | 5/6/2026 | Transformer Models |
| 11 | 5/13/2026 | Search Problems |
| 12 | 5/20/2026 | Reinforcement Learning |
| 13 | 5/27/2026 | AI Ethics and Safety |
| 14 | 6/3/2026 | AI for Science |
| 15 | 6/10/2026 | Course Summary |
| 17 | 6/21/2026 | Final Exam |
The courses and teaching materials are mainly developed by Prof. Xingjun Ma.
Teaching Assistants
This course is supported by two teaching assistants:
- Lei Liu — liulei.yossarian(at)gmail.com
- Weidong Guo — gwd200(at)mail.ustc.edu.cn
Recommended Reading Materials
Reuse
Citation
For attribution, please cite this work as:
Li, Zeju. 2026. “AI Fundamentals 2026.” March 1, 2026. https://zerojumpline.github.io//teaching/2026-03-01-AI.