CSCI/ARTI 8950: Machine Learning (Spring 2020)

Instructor Sheng Li
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 in-depth 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.


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%)
In-class 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 follow-up works on the topic of the assigned paper (e.g., search the latest papers that cite the assigned paper), summarize the state-of-the-art 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 3-3.3 Instructor
3 01/20 (M) No Class; Holiday HW1 OUT
01/21 (T) Clustering CIML 3.4-3.5 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 7-7.4 Instructor
5 02/03 (M) Linear Models: Gradient Descent CIML 7.4-7.7 Instructor
02/04 (T) Probabilistic Models (I) CIML 9-9.5 Instructor
02/06 (R) Probabilistic Models (II) CIML 9.6-9.7 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 10-10.3 Instructor HW2 DUE (11:59 PM)
02/18 (T) Neural Networks (II) CIML 10.4-10.6 Instructor
02/20 (R) Midterm Review Instructor
8 02/24 (M) Deep Learning (I) Instructor Review-1 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 Project--Final Presentation (I) Students
04/21 (T) Course Project--Final Presentation (II) Students
04/23 (R) Course Project--Final Presentation (III) Students Paper Review Due: 04/27
17 04/27 (M) Course Review and Discussion Final Report Due: 04/28