| Instructor | Sheng Li E-mail: sheng.li@uga.edu |
| Time and Location of Lecture | TR: 12:30 pm - 1:45 pm Chemistry 551 W: 12:20 pm - 1:10 pm Dawson Hall 208 |
| Instructor Office Hours and Location | Wednesday: 1:10 pm - 2:30 pm or by an email appointment. Boyd GSRC 549 |
| TA Office Hours and Location | TA: Hiten Nirmal (hn97292@uga.edu) Monday: 1:15 pm - 2:15 pm Wednesday: 11:15 pm - 12:15 pm LAB 307, Boyd GSRC |
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. 9 (W) | Course Overview | |
| Jan. 10 (R) | Introduction to Data Science | ||
| 2 | Jan. 15 (T) | Data Collection | |
| Jan. 16 (W) | Data Preprocessing | ||
| Jan. 17 (R) | Data Visualization | ||
| 3 | Jan. 22 (T) | Data Visualization | |
| Jan. 23 (W) | Data Visualization | HW1 OUT | |
| Jan. 24 (R) | Review of Linear Algebra and Statistics | ||
| 4 | Jan. 29 (T) | Python Programming (I) | Guest Speaker |
| Jan. 30 (W) | Python Programming (II) | Guest Speaker | |
| Jan. 31 (R) | Python Libraries for Data Science | Guest Speaker | |
| 5 | Feb. 5 (T) | Linear Regression | HW1 DUE (11:59 PM) |
| Feb. 6 (W) | Model Selection | ||
| Feb. 7 (R) | Ridge regression and Lasso | ||
| 6 | Feb. 12 (T) | Basic Classification Models | |
| Feb. 13 (W) | Basic Classification Models | ||
| Feb. 14 (R) | Basic Classification Models | HW2 OUT | |
| 7 | Feb. 19 (T) | Basic Classification Models | |
| Feb. 20 (W) | Basic Classification Models | ||
| Feb. 21 (R) | Project Proposal Presentation | ||
| 8 | Feb. 26 (T) | Project Proposal Presentation | HW2 Due (11:59 PM) |
| Feb. 27 (W) | Midterm Review | ||
| Feb. 28 (R) | Advanced Classification Models | ||
| 9 | Mar. 5 (T) | Midterm | HW3 OUT |
| Mar. 6 (W) | Advanced Classification Models | ||
| Mar. 7 (R) | Advanced Classification Models | ||
| 10 | Mar. 12 (T) | Spring Break; No Class | |
| Mar. 13 (W) | Spring Break; No Class | ||
| Mar. 14 (R) | Spring Break; No Class | ||
| 11 | Mar. 19 (T) | Advanced Classification Models | HW3 DUE (11:59 PM) |
| Mar. 20 (W) | Advanced Classification Models | ||
| Mar. 21 (R) | Project Progress Review | HW4 OUT | |
| 12 | Mar. 26 (T) | Clustering | |
| Mar. 27 (W) | Clustering | ||
| Mar. 28 (R) | Clustering | ||
| 13 | Apr. 2 (T) | Clustering | HW4 DUE (11:59 PM) |
| Apr. 3 (W) | Dimensionality Reduction | ||
| Apr. 4 (R) | Dimensionality Reduction | HW5 OUT | |
| 14 | Apr. 9 (T) | Dimensionality Reduction | |
| Apr. 10 (W) | Feature Selection | ||
| Apr. 11 (R) | Feature Selection | ||
| 15 | Apr. 16 (T) | Neural Networks and Deep Learning | HW5 DUE (11:59 PM) |
| Apr. 17 (W) | Neural Networks and Deep Learning | ||
| Apr. 18 (R) | Neural Networks and Deep Learning | ||
| 16 | Apr. 23 (T) | Team Project Presentation (I) | |
| Apr. 24 (W) | Team Project Presentation (II) | ||
| Apr. 25 (R) | Team Project Presentation (III) | ||
| 17 | Apr. 30 (T) | Course Review | |