Summer Course in Peking University
This is an advanced undergraduate course on the introduction of basic mathematical and practical aspects of deep learning techniques. The course will cover some basic deep learning models such as convolutional neural networks, training algorithms such as stochastic gradient descent methods, popular data bases such as MNIST and CIFAR and specific applications such as image classifications. Examples of other successful applications of deep learning will also be briefly discussed for various tasks of machine learning and artificial intelligence such as computer vision, natural language processing and reinforcement learning. More specifically, the following topics will be covered:
- Basics of machine learning, logistic regression and SVM
- Deep neural networks and mathematical properties
- Multigrid methods and convolutional neural networks
- Stochastic gradient descent methods and convergence analysis
- PyTorch and deep learning for image classification
- Other applications
The course will be held at Peking University during July 6-27, 2019. Lectures and lab hours will be given 8am-9:30am and 2pm-4pm and Q&A sessions will be 10:00am-11:30am every weekday. Students are expected to practice and do assignments during the afternoon sessions. Typed lecture notes will be distributed in class. Both math and computing homework problems will be assigned. We will have four homeworks in the first two weeks, a mid-term and a final project.
Prof. Jinchao Xu (Penn State)
- Guest lecturers:
Prof. Zhanxin Zhu (Peking University) on July 16-17 and
Prof. Bin Dong (Peking University) on July 23-24
- Jonathan Siegel (Penn State)
- Juncai He (Penn State)
- Jianhong Chen (Penn State)
- Xiaodong Jia (Peking University)
- Yuyan Chen (Peking University)
- Huang Huang (Peking University)
Course Time and Location
- Three weeks for 3 credits: July 6-27, 2019
- Time: 8:00-9:30 am and 2:00-3:30 pm
- Place: Room 408 in the Third Teaching Building(三教408), Peking University, Beijing China
- Assignment #1: 10%
- Assignment #2: 10%
- Assignment #3: 10%
- Assignment #4: 10%
- Midterm: 20%
- Course Project: 40%
- Course: Math 497, An Introduction to Deep Learning
- Coordinator: Shaobo Liang and Li Jiang (Peking University)
- Staff Assistant: Shenglan Zhao (firstname.lastname@example.org)
- For a detailed timetable of the course, see Timetable
Notes and homeworks
- Heming Liu: We become enthusiastic because you were excited to teach. I really appreciate that you gave us this precious opportunity to taking class in Peking University. Through these three weeks, I did learn a lot. I have more mathematical understanding about the CNN structure and my programming skills are getting better and better.
- Shuhan Li: Professor Xu is very helpful, and care for every student. He always asked us if there are any questions or if he talked too fast. With the limit of knowledge, we always feel a little ashamed to ask such a simple question on class, but he is so nice and encouraged us a lot, also talked with us after class, which give me more motivation to learn.
- Zhenghan Fang: This class is a great opportunity for me to appreciate math and see real examples of how useful it can be.
- Zihang Weng: I had a great time learning the math behind it, also I picked up the coding in just three weeks, thanks to your graduate and postdoc’s help.