Course Description

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.

Course Lecturer

Lecturer:

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

Teaching Assistants

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

Grading Policy

Assignment #1: 10%

Assignment #2: 10%

Assignment #3: 10%

Assignment #4: 10%

Midterm: 20%

Course Project: 40%

Course Related

Course: Math 497,  An Introduction to Deep Learning

Coordinator: Shaobo Liang and Li Jiang (Peking University)

Staff Assistant: Shenglan Zhao (slzhao@pku.edu.cn)

Notes and homeworks

Homework 1.  HW1

Homework 2. HW2

Homework 3. HW3

Homework 4. HW4

Mid-term.

Final project.

Students impressions

Student 1: We become enthusiastic because all the professors  were excited to teach. I really appreciate that they 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.

Student 2:  Our professor is very helpful, and cares 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.

Student 3: This class is a great opportunity for me to appreciate math and see real examples of how useful it can be.

Student 4:  I had a great time learning the math behind it, also I picked up the coding in just three weeks, thanks to all the graduate and postdoc’s(TA) help.