Instructor  Sheng Li Email: sheng.li@uga.edu 
Time and Location of Lecture  M: 2:30 pm  3:20 pm Boyd GSRC 208 TR: 2:00 pm  3:15 pm Boyd GSRC 208 
Office Hours and Location  Thursday: 1 pm  2 pm or by an email appointment. Boyd GSRC 804 
Course Description
This course provides students with an indepth introduction to machine learning theory, models and applications. The course covers classical machine learning algorithms for clustering and classification, and also discusses some emerging issues in machine learning such as fairness.
Please refer to the course syllabus for more details.
Recommended Background
CSCI 6380 (Data Mining), CSCI 6550 (Artificial Intelligence), CSCI 6560 Evolutionary Computation, or Permission of Department.
Grading
Section  Portion  Description 

Homework  10%  
Paper Review  10%  Write comprehensive paper reviews for assigned papers 
Paper Presentation  10%  Present assigned papers and lead discussion 
Midterm Exam  30%  
Team Project  35%  Project proposal (10%); Progress review (5%); Final presentation (10%) and report (10%) 
Inclass Participation  5% 
Grade Conversion Table:
Letter Grade  A  A  B+  B  B  C+  C  C  D+  D  D  F 

Range  [93,100]  [90,92]  [87,89]  [83,86]  [80,82]  [77,79]  [73,76]  [70,72]  [67,69]  [63,66]  [60,62]  [0,59] 
Academic Honesty
We will strictly follow UGA’s Academic Honesty Policy. Dishonest behavior will not be tolerated and may result into failing the course. Please contact the instructor if you have any concerns regarding this issue.
Paper Review and Presentation
Note: Please sign up for paper presentations (Deadline: 11:59pm, January 17, 2020)
Paper Review: Students will be required to review 5 papers over the semester and submit the review to eLC by 11:59PM of the due date shown in class schedule below. The reviewed paper should be chosen from the papers that will be presented in the following classes (Refer to class schedule for more details). The reviewed papers should not contain the papers 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 possible future research directions.
Paper Presentation: Each student will be required to present one research paper over the semester. Each presenter should prepare slides for a 25 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) at least 3 questions for discussion. The presenter will need to lead another 10 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  Readings  Presenters  Notes 

1  01/07 (T)  Course Overview  N/A  Instructor  
01/09 (R)  An Overview of Machine Learning  Instructor  
2  01/13 (M)  Decision Trees  CIML 1  Instructor  
01/14 (T)  Limits of Learning  CIML 2  Instructor  
01/16 (R)  Geometry and Nearest Neighbors  CIML 33.3  Instructor  
3  01/20 (M)  No Class; Holiday  HW1 OUT  
01/21 (T)  Clustering  CIML 3.43.5  Instructor  
01/23 (R)  Perceptron  CIML 4  Instructor  
4  01/27 (M)  Multiclass Classification  CIML 6  Instructor  HW1 DUE (11:59 PM) 
01/28 (T)  Bias and Fairness  CIML 8  Instructor  
01/30 (R)  Linear Models  CIML 77.4  Instructor  
5  02/03 (M)  Linear Models: Gradient Descent  CIML 7.47.7  Instructor  
02/04 (T)  Probabilistic Models (I)  CIML 99.5  Instructor  
02/06 (R)  Probabilistic Models (II)  CIML 9.69.7  Instructor  HW2 OUT Project Proposal Due: 02/07 

6  02/10 (M)  Project Proposal Presentation (I)  Students  
02/11 (T)  Project Proposal Presentation (II)  Students  
02/13 (R)  Project Proposal Presentation (III)  Students  
7  02/17 (M)  Neural Networks (I)  CIML 1010.3  Instructor  HW2 DUE (11:59 PM) 
02/18 (T)  Neural Networks (II)  CIML 10.410.6  Instructor  
02/20 (R)  Midterm Review  Instructor  
8  02/24 (M)  Deep Learning (I)  Instructor  Review1 Due for papers: 03/02, 03/03, 03/05  
02/25 (T)  Deep Learning (II)  Instructor  
02/27 (R)  Midterm  
9  03/02 (M)  Paper Presentation: Linear Models  Students  
03/03 (T)  Paper Presentation: Linear Models  Students  
03/05 (R)  Paper Presentation: Linear Models  Students  
10  03/09 (M)  No Class; Spring Break  
03/10 (T)  No Class; Spring Break  
03/12 (R)  No Class; Spring Break  
11  03/16 (M)  Paper Presentation: Clustering  Students  
03/17 (T)  Paper Presentation: Clustering  Students  
03/19 (R)  Paper Presentation: Clustering  Students  
12  03/23 (M)  Paper Presentation: Probabilistic Models  Students  
03/24 (T)  Project Progress Update  Students  
03/26 (R)  Paper Presentation: Probabilistic Models  Students  
13  03/30 (M)  Paper Presentation: Neural Networks  Students  
03/31 (T)  Paper Presentation: Neural Networks  Students  
04/02 (R)  Paper Presentation: Neural Networks  Students  Project Update Due (by email): 04/03  
14  04/06 (M)  Paper Presentation: Fairness in ML  Students  
04/07 (T)  Paper Presentation: Fairness in ML  Students  
04/09 (R)  Paper Presentation: Fairness in ML  Students  
15  04/13 (M)  Paper Presentation: Interpretable ML  Students  
04/14 (T)  Paper Presentation: Interpretable ML  Students  
04/16 (R)  Paper Presentation: Interpretable ML  Students  Final Project Slides Due: 04/17  
16  04/20 (M)  Course ProjectFinal Presentation (I)  Students  
04/21 (T)  Course ProjectFinal Presentation (II)  Students  
04/23 (R)  Course ProjectFinal Presentation (III)  Students  Paper Review Due: 04/27  
17  04/27 (M)  Course Review and Discussion  Final Report Due: 04/28 