08408005 Deep Learning: Algorithm and Application

Instructor:

Prof. Yadong MU : myd@pku.edu.cn

Teaching Assistant:

Mr. LI Jinghan : li.jh@stu.pku.edu.cn
Mr. MU Yanchen : muyanchen@stu.pku.edu.cn

Location: Room 208, Teaching Building 1, Peking University

Time: Wednesday 18:40am - 21:30pm (weekly)

Office hours: Drop an email or wechat message to the instructor or TAs for appointing a course-related face-to-face QA.

Schedule of Lectures

Date	Topics
Feb 20, 2025	Introduction Course logistics A short tour to deep learning: illustrative applications, brief history etc.
Feb 27, 2025	Machine Learning Basics Machine learning paradigms Supervised learning: linear case Supervised learning: non-linear case Gradient Descent Stochastic Gradient Descent Unsupervised learning
March 6, 2025	Deep Learning Basics Neural layers: convolution, pooling, FC, activation, loss functions Gradient back-propagation Auto-differentiation Regularization
March 13, 2025	Backbone - I Conventional Image Classification AlexNet, VGGNet, GoogleNet, ResNet, DenseNet Squeeze-and-Excitation, GroupConv, Depthwise / Depthwise Separable Convolution, ShuffleNet
March 20, 2025	Backbone - II Classic Object Detection Z-F Net RCNN family: RCNN, Fast RCNN, Faster RCNN YOLO, SSD RetinaNet, FPN etc
March 27, 2025	Backbone - III Fully convolutional networks (FCN) Encoder-Decoder (SegNet), DeepLab Contextual inference with graphical models (CNN-as-RNN) Mask R-CNN Generalization to other pixel-to-pixel tasks Resolution matters: Simple Baseline & HRNet
April 3, 2025	Deep Models for Sequential Data RNN LSTM Transformer MLP-Mixer Mamba
April 10, 2025	Foundation Models
April 17, 2025	Generative Models
April 24, 2025	Reinforcement Learning
May 1, 2025	Holiday - no class
May 8, 2025	Learning on Graphs / Adversarial and Backdoor Learning
May 15, 2025	DL for Science / Optimization
May 22, 2025	Course Project Presentation - I
May 29, 2025	Course Project Presentation - II
June 5, 2025	Course Project Presentation - III

Textbook:

Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016

References:

Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006
Shai Ben-David and Shai Shalev-Shwartz, Understanding Machine Learning: From Theory to Algorithms, 2014
Richard Sutton, Reinforcement Learning, 2nd edition
Zhi-hua Zhou, Machine Learning (in Chinese), 2016

Course Work

Final Grade	Grading will be based on homeworks (35%), survey (25%) and a course project (40%). The end-of-term grade is curved. Your overall grade will depend on your performance relative to your classmates.
Survey & Project	The topic of survey paper should be DL-related. A Course Project is mandatory for all students. The only requirement for the topic is being deep learning related.