Instructor
Dr. Sheng Li
Email: sheng.li@uga.edu
Time and Location of Lectures
TR: 11:00 am  12:15 pm
W: 11:15 am  12:05 pm
Boyd GSRC 306
Office Hours and Location
Thursday: 10:00  11:00 am or by an email appointment.
Boyd GSRC 549
Course Description
The purpose of this course is to familiarize students with several advanced topics in machine learning, including representation learning, multiview learning, transfer learning, active learning, and counterfactual learning. Realworld applications
of these machine learning approaches will also be covered in this course, such as data clustering, image classification, human action recognition, outlier detection, recommendation system, online advertising, etc.
This seminartype course will be research oriented, encouraging students to explore the recent advances in machine learning field. The instructor will review the basic concepts of machine learning and briefly introduce some advanced topics.
After that, students will in turn present research papers from the reading materials. In addition, students will need to work on a research project on machine learning theory, methodology, or applications.
Please refer to the
course syllabus
for more details.
Paper Review and Presentation
Note : Please sign up for paper review and presentations. ( Deadline: 11:59pm, Aug. 19)
Paper Review: Students will be required to read 10 papers from the reading list and submit a brief review of each paper to the instructor by midnight (12:00am) before the scheduled lecture. The 10 papers should not contain the paper you’re going to present in the class. The review should summarize the main idea and contributions of the paper, describe the major experimental results, and discuss the strengths and weaknesses of the paper. The students are also encouraged to check the followup works on the topic of the assigned paper (e.g., search the latest papers that cite the assigned paper), summarize the stateoftheart methods and results, and discuss their limitations and possible future research directions.
Pape Presentation: Each student will be required to present one research paper over the semester. Each presenter should prepare slides for a 3040 minutes talk on the paper. Slides for the talk must be emailed to the instructor by midnight (12:00am) before the class. The talk should clearly address the following points: (1) motivation and problem statement; (2) related work; (3) methodology; (4) experiments; (4) conclusions; and (5) discussions. The presenter will need to lead another 1020 minutes discussion during or after the talk. The presenter should prepare discussion questions that lead to a deeper analysis of the paper’s content, strengths, weaknesses, and future works.
Class Schedule (Tentative)
Week  Date  Topic  Paper  Presenter  Notes 

1  08/14 (T)  Course Overview  N/A  Instructor  [Slides] 
08/15 (W)  An Overview of Machine Learning  N/A  Instructor  [Slides]  
08/16 (R)  An Overview of Deep Learning  N/A  Instructor  [Slides]  
2  08/21 (T)  Dimensionality Reduction Unsupervised Learning  Sparse Manifold Clustering and Embedding (NIPS 2011)  Instructor  
08/22 (W)  Unsupervised Learning  Accelerating tSNE using TreeBased Algorithms (JMLR, 2014)  Weifeng Wang  
08/23 (R)  Unsupervised Learning  Clustering Millions of Faces by Identity (IEEE Trans. PAMI, 2018)  Marcus Hill  
3  08/28 (T)  Unsupervised Learning
Information Theory 
Fixing a broken ELBO (ICML'18)  Bahaa  
08/29 (W)  Multiview Learning 
MultiView Clustering via Canonical Correlation Analysis (ICML 2009)
MultiView Intact Space Learning (IEEE Trans. PAMI, 2015) 
Instructor  
08/30 (R)  Multiview Learning  Beyond CCA: Moment Matching for MultiView Models (ICML 2016)  Instructor  [Course Project]  
4  09/04 (T)  Transfer Learning  An embarrassingly simple approach to zeroshot learning (ICML 2015)  Instructor  
09/05 (W)  Transfer Learning  Learning Transferrable Representations for Unsupervised Domain Adaptation (NIPS 2016)  Sili Wang  
09/06 (R)  Transfer Learning  Transfer Learning via Learning to Transfer (ICML 2018)  Instructor  
5  09/11 (T)  Deep Learning for Computer Vision  Unpaired imagetoimage translation using cycleconsistent adversarial networks (ICCV 2017)  Instructor  
09/12 (W)  Deep Learning for Computer Vision  Densely Connected Convolutional Networks (CVPR 2017)  Zahra Jandaghi  
09/13 (R)  Deep Learning for Computer Vision  Deep Residual Learning for Image Recognition (CVPR 2016)  Abolfazl Farahani  
6  09/18 (T)  Deep Learning for Computer Vision  Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge (IEEE Trans. PAMI, 2017)  Instructor  
09/19 (W)  Deep Learning for Computer Vision  Detecting Visual Relationships with Deep Relational Networks (CVPR 2017)  Instructor  
09/20 (R)  Guest Lecture  N/A  Jiasen Lu (Georgia Tech)  
7  09/25 (T)  Project Proposal Presentation (I)  N/A  Students  
09/26 (W)  Project Proposal Presentation (II)  N/A  Students  
09/27 (R)  Project Proposal Presentation (III)  N/A  Students  
8  10/02 (T)  Deep Learning for NLP 
GloVe: Global Vectors for Word Representation (EMNLP 2014) [Presentation]
Sequence to Sequence Learning with Neural Networks (NIPS 2014) [Discussion only] 
Xuan Zhang  
10/03 (W)  Deep Learning for NLP  Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (ICML 2016)  Saber Soleymani  
10/04 (R)  Deep Learning for NLP  Characterlevel Convolutional Networks for Text Classification (NIPS 2015)  Aishwarya Jagtap  
9  10/09 (T)  Deep Learning for NLP 
Attention Is All You Need (NIPS 2017) [Presentation]
A KnowledgeGrounded Neural Conversation Model (AAAI 2018) [Discussion only] 
Dharamendra Kumar  
10/10 (W)  Deep Learning for User Modeling  A MultiView Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (WWW 2015)  Mohammad alSaad  
10/11 (R)  Deep Learning for User Modeling 
Latent Cross: Making Use of Context in Recurrent Recommender Systems (WSDM 2018) [Presentation]
Neural Collaborative Filtering (WWW 2017) [Discussion only] 
Yuhua Shi  
10  10/16 (T)  Network Representation 
DeepWalk: Online Learning of Social Representations (KDD '14) [Presentation] LINE: Largescale Information Network Embedding (WWW '15) [Discussion only] 
Saed Rezayi  
10/17 (W)  Network Representation  node2vec: Scalable Feature Learning for Networks (KDD '16)  Sumer Singh  
10/18 (R)  Guest Lecture  A Survey on Truth Discovery (ACM SIGKDD Explorations) When Truth Discovery Meets Medical Knowledge Graph: Estimating Trustworthiness Degree for Medical Knowledge Condition (arXiv) 
Dr. Yaliang Li (Tencent America)  
11  10/23 (T)  Graph Convolutional Networks  SemiSupervised Classification with Graph Convolutional Networks (ICLR 2017)  Xiaodong Jiang  
10/24 (W)  Graph Convolutional Networks  Inductive representation learning on large graphs (NIPS 2017)  An Chen  
10/25 (R)  Graph Convolutional Networks  Graph Attention Networks (ICLR 2018)  Yang Shi  
12  10/30 (T)  Adversarial Training  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (arXiv, 2015)  Jiaqi Wang  
10/31 (W)  Adversarial Training  Wasserstein Generative Adversarial Networks (ICML 2017)  Ankita Joshi  
11/01 (R)  Adversarial Training  Virtual adversarial training: a regularization method for supervised and semisupervised learning (IEEE Trans. PAMI, 2018)  Jiaojiao Wang  
13  11/06 (T)  Neural Architecture Search  Neural Architecture Search with Reinforcement Learning (ICLR 2017)  Ailing Wang  
11/07 (W)  Course ProjectStatus Update  TBA  Students  
11/08 (R)  Neural Architecture Search  Progressive Neural Architecture Search (ECCV 2018)  Xingchen Jian  
14  11/13 (T)  Machine Learning Meets Causal Inference  TBA  Instructor  
11/14 (W)  Machine Learning for Healthcare  TBA  Instructor  
11/15 (R)  Guest Lecture  Reinforcement Learning and Inverse Reinforcement Learning  Prof. Prashant Doshi (UGA)  
15  11/20 (T) 


11/21 (W) 


11/22 (R) 


16  11/27 (T)  Interpretable Machine Learning  TBA  Instructor  
11/28 (W)  Course ProjectFinal Presentation (I)  N/A  Students  
11/29 (R)  Course ProjectFinal Presentation (II)  N/A  Students  
17  12/04 (T)  Course ProjectFinal Presentation (III)  N/A  Students 