Spring 2026
Neural Networks and Deep Learning
Instructor: Yu Wang
Course Description
This course covers fundamental concepts of machine learning, feedforward neural networks, convolutional neural networks, recurrent neural networks, network optimization and regularization, as well as attention mechanisms and external memory. Designed for mathematics majors, the course emphasizes both theoretical foundations and practical applications.
Assessment
Homework and experiments
30%
Project
20%
Final exam
50%
Main Topics
- 01 Fundamental concepts of machine learning
- 02 Feedforward neural networks
- 03 Convolutional neural networks (CNN) and their applications
- 04 Recurrent neural networks (RNN) and their variants
- 05 Network optimization and regularization
- 06 Attention mechanisms and external memory
- 07 Transformer
- 08 Cutting-edge technology: LLM, PINN, Agent
Course Materials
Recommended Reference
Xipeng Qiu. Neural Networks and Deep Learning. China Machine Press, 2020. (in Chinese)
Student Excellence
Hall of Fame
If you have any questions, please contact: yuwangmath at 163.com