Instructor | Sheng Li E-mail: sheng.li@uga.edu |
Time and Location of Lecture | M: 4:10 pm - 5:00 pm Online TR: 3:55 pm - 5:10 pm Online |
Instructor Office Hours | Monday: 3:00 pm - 4:00 pm or by an email appointment. |
TA Office Hours and Location | TA: Saed Rezayi (E-mail: saedr@uga.edu) Time: 4PM-5PM, Wednesdays. Zoom link is available on eLC. |
Course Description
This course presents a rigorous overview of methods for data mining, image processing, natural language processing, and scientific computing. Core concepts in supervised and unsupervised analytics, dimensionality reduction, deep learning, and data visualization will be explored in depth. Please refer to the syllabus for more information.
Textbooks
The main textbook for this course is:
“An Introduction to Statistical Learning with Applications in R” by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. Springer.
The PDF version of this book is available on the author's homepage.
Grading
Section | Portion | Description |
---|---|---|
Homework | 35% | 5 individual assignments involving problem solving and programming |
Exams | 35% | Midterm (15%) and Final (20%) |
Team Project | 25% | Project proposal (5%); Progress review (5%); Final presentation and report (15%) |
In-class Participation | 5% |
Homework Submission: Homework should be submitted to the eLC by due date (11:59pm).
Late Submission Policy: Late submissions will be penalized by deducting 10% of the score for each day beyond due time.
Exams: Both exams are closed-books/notes.
Grade Conversion Table:
Letter Grade | A | A- | B+ | B | B- | C+ | C | C- | D+ | D | D- | F |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Range | [93,100] | [90,93) | [87,90) | [83,87) | [80,83) | [77,80) | [73,77) | [70,73) | [67,70) | [63,67) | [60,63) | [0,60) |
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.
Class Schedule (Tentative)
Week | Date | Topic | Notes |
---|---|---|---|
1 | Jan. 14 (R) | Course Overview | |
2 | Jan. 18 (M) | Holiday; No Class | |
Jan. 19 (T) | Introduction to Data Science | ||
Jan. 21 (R) | Python Programming (I) | ||
3 | Jan. 25 (M) | Python Programming (II) | |
Jan. 26 (T) | Python Libraries for Data Science | ||
Jan. 28 (R) | Data Collection | ||
4 | Feb. 1 (M) | Data Preprocessing | |
Feb. 2 (T) | Data Visualization | HW1 OUT | |
Feb. 4 (R) | Review of Linear Algebra and Statistics | ||
5 | Feb. 8 (M) | Linear Regression | |
Feb. 9 (T) | Model Selection | ||
Feb. 11 (R) | Ridge regression and Lasso | HW1 DUE (11:59 PM) | |
6 | Feb. 15 (M) | Basic Classification Models | |
Feb. 16 (T) | Basic Classification Models | HW2 OUT | |
Feb. 18 (R) | Basic Classification Models | ||
7 | Feb. 22 (M) | Basic Classification Models | |
Feb. 23 (T) | Basic Classification Models | ||
Feb. 25 (R) | Project Proposal Presentation | HW2 Due (11:59 PM) | |
8 | Mar. 1 (M) | Project Proposal Presentation | |
Mar. 2 (T) | Advanced Classification Models | HW3 OUT | |
Mar. 4 (R) | Midterm Review | ||
9 | Mar. 8 (M) | Advanced Classification Models | |
Mar. 9 (T) | Midterm | ||
Mar. 11 (R) | Advanced Classification Models | ||
10 | Mar. 15 (M) | Advanced Classification Models | HW3 DUE (11:59 PM) |
Mar. 16 (T) | Advanced Classification Models | ||
Mar. 18 (R) | Clustering | HW4 OUT | |
11 | Mar. 22 (M) | Clustering | |
Mar. 23 (T) | Clustering | ||
Mar. 25 (R) | Clustering | ||
12 | Mar. 29 (M) | Dimensionality Reduction | HW4 DUE (11:59 PM) |
Mar. 30 (T) | Project Progress Review | ||
Apr. 1 (R) | Dimensionality Reduction | HW5 OUT | |
13 | Apr. 5 (M) | Dimensionality Reduction | |
Apr. 6 (T) | Feature Selection | ||
Apr. 8 (R) | Instructional Break; No Class | ||
14 | Apr. 12 (M) | Feature Selection | |
Apr. 13 (T) | Neural Networks and Deep Learning | ||
Apr. 15 (R) | Neural Networks and Deep Learning | HW5 DUE (11:59 PM) | |
15 | Apr. 19 (M) | Neural Networks and Deep Learning | |
Apr. 20 (T) | Neural Networks and Deep Learning | ||
Apr. 22 (R) | Team Project Presentation (I) | ||
16 | Apr. 26 (M) | Team Project Presentation (II) | |
Apr. 27 (T) | Team Project Presentation (III) | ||
Apr. 29 (R) | Course Review | ||