CSCI 4990: Data Science Capstone (Spring 2024)

Course Information

  • Instructor: Ninghao Liu

  • Course time and location:

    • TR: 9:35 am - 10:50 am, Miller Plant Sci 1102

  • Office hours: Thursday, 2:00 PM - 3:00 PM

  • Office: Boyd 616

Course Description

This capstone course has several goals: to provide you a platform for learning variety of advanced data science techniques, to give you experience working with a real data set, and to help practice your communication skills – both written and oral.

Textbooks

The main textbook (non-mandatory) for this course is:

Dive into Deep Learning” by Aston Zhang, Alexander J. Smola, Zachary Lipton, Mu Li.

Other textbooks: “Data Mining: Concepts and Techniques, 3rd edition” by Jiawei Han, Micheline Kamber, Jian Pei.

Course Prerequisite

Courses related to data mining, machine learning, or deep learning. Students are assumed to be familiar with calculus, linear algebra and python programming.

Grading

Letter Grade A A- B+ B B- C+ C C- D F
Range [90, 100] [87, 90) [84, 87) [80, 84) [77, 80) [74, 77) [70, 74) [67, 70) [60, 67) [0, 60)

Late Submission Policy: For homework assignments, 20% is deducted for each late day for up to 48 hours (including weekends) after which submissions are not accepted. Late presentation materials and project reports not accepted.

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.

Course Schedule (Tentative)

Week Date Topic Notes
1 01/09 Course Overview
01/11 Student Presentation: Self-introduction Start to find teammates
2 01/16 Linear models
01/18 Linear models
3 01/23 Presentation preparation
01/25 Presentation 1A Python and Pytorch programming
4 01/30 Presentation 1B Decision trees
02/01 Presentation 1C Neural networks
5 02/06 Presentation 1D Neural networks
02/08 Presentation 1E Training and evaluation
6 02/13 Presentation preparation
02/15 Presentation 2A Advanced models
7 02/20 Presentation 2B Advanced models
02/22 Presentation 2C Advanced models
8 02/27 Presentation 2D Advanced models
02/29 Presentation 2E Advanced models
9 03/05 - Spring Break. No class.
03/07 - Spring Break. No class.
10 03/12 Presentation preparation
03/14 Presentation 3A
11 03/19 Presentation 3B
03/21 Presentation 3C
12 03/26 Presentation 3D
03/28 Presentation 3E
13 04/02 Presentation preparation
04/04 Presentation 4A Project related
14 04/09 Presentation 4B Project related
04/11 Presentation 4C Project related
15 04/16 Presentation 4D Project related
04/18 Presentation 4E Project related
16 04/23 Final Presentation
04/25 Final Presentation
17 05/03 - Final report due