Machine Learning (Fall 2021)

Administrative Matters

Instructor: Lin ZHANG (张林)

TA: Tianjun ZHANG


Office: RM408L, Jishi Building, Jiading Campus


Lecture Slides



Reading Materials

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AdaBoost and Cascade Structure

1. P. Viola and M.J. Jones, Robust real-time face detection, IJCV' 04

2. Y. Freund and R.E. Schapire, A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting, Journal of Computer and System Sciences,1995


Principal Component Analysis

1. M. Turk and A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience' 91

2. PCADemo: a matlab program used in our lectures to demonstrate the basic concepts related to PCA
3. FaceRecByEigenFace: a matlab demo composed by me to illustrate how to make use of the eigen-face approach to perform face recognition. Very simple and straightforward. (You need to run "training.m" at first)

Sparse Representation based Classification

1. J. Wright et al., Robust face recognition via sparse representation, IEEE PAMI' 09
2. L. Zhang et al., Sparse representation or collaborative representation: which helps face recognition?, ICCV' 11
3. Implementations of several l1-minization solvers, provided by Allen Yang (EECS, Berkeley)
4. CRC_RLS: a matlab demo program implementing the CRC_RLS based face recognition method.

Linear Models


Neural Networks and CNN

1. K. He et al., Deep Residual Learning for Image Recognition, CVPR 2016

2. G. Huang et al., Densely Connected Convolutional Networks, CVPR 2017

3. J. Redmon et al., Yolo: 9000 better, faster, stronger, CVPR 2017

4. N. Ma et al., ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design, ECCV 2018

5. Learn to configure YoloV2 and try to solve your own detection task,

6. 典型卷积神经网络模型结构的演进

Least Squares

1. K. Madsen, H.B. Nielsen, O. Tingleff, Methods for nonlinear least squares, Technical University of Denmark, 2004

2. Lin Zhang, Understanding Singular Value Decomposition, Tongji University, 2020

Visual Perception Practices in Autonomous Driving

1.  Tianjun Zhang, Nlong Zhao, Ying Shen, Xuan Shao, Lin Zhang*, and Yicong Zhou, “ROECS: A Robust Semi-direct Pipeline Towards Online Extrinsics Correction of the Surround-view System”, in: Proc. ACM MM, pp. 3153-3161, 2021.

2.  Xuan Shao, Lin Zhang*, Tianjun Zhang, Ying Shen, Hongyu Li, and Yicong Zhou, “A Tightly-coupled Semantic SLAM System with Visual, Inertial and Surround-view Sensors for Autonomous Indoor Parking”, in: Proc. ACM MM, pp. 2691–2699, 2020.

3.  Lin Zhang et al., "Vision-based parking-slot detection: A DCNN-based approach and a large-scale benchmark dataset"IEEE Trans. Image Processing, vol. 27, no. 11, pp. 5350-5364, 2018.

GANs and Their Applications in Image Generation

1.   I.J. Goodfellow et al., Generative adversarial nets, NIPS, 2014

2.   A. Radford et al., Unsupervised representation learning with deep convolutional generative adversarial networks, ICLR, 2016

3.   M. Arjovsky et al., Towards principled methods for training generative adversarial networks, ICLR, 2017

4.   M. Arjovsky et al., Wasserstein GAN, arXiv, 2017

5.   I. Gulrajani et al., Improved training of Wasserstein GANs, arXiv, 2017

6.   P. Isola, J. Zhu, T. Zhou, and A.A. Efros, Image-to-image translation with conditional adversarial networks, CVPR, 2017

7.   J. Zhu et al., Unpaired image-to-image translation using cycle-consistent adversarial networks, arXiv, 2017

8.   C. Ledig et al., Photo-realistic single image super-resolution using a generative adversarial network, CVPR, 2017

9.   A. Shrivastava et al., Learning from simulated and unsupervised images through adversarial training, CVPR, 2017




1. Assignment 1, Due: Oct. 24, 2021 (marks for class 1, marks for class 2)

2. Assignment 2, Due: Dec. 05, 2021 (testvideo)



1. Compress all files into a .rar file whose name is composed of student name and ID.

2. For the programming assignments, please make sure your program can successfully run on TA's machine.

3. All the documents you hand in, including comments in the source codes, should be in English.

4. Please send your solutions to TA  and confirm with TA that she has received your email successfully.



Created on: Aug. 20, 2021

Last updated on: Nov. 18, 2021