Ricardo Vilalta (r.vilalta.us@ieee.org)
Durga D. and Sushila Agrawal Engineering Research Bldg. Room 203C.
BY appointment.
We will meet Tuesdays and Thursdays at 2:30 PM CT via MS Teams.
(713) 743-3614
| Farzana Jasmin |
|---|
| Office Hours: Wednesdays 10:00 - 11:00 AM (MS Teams) |
| Email: fyasmin2@cougarnet.uh.edu |
This course offers advanced modeling and analysis techniques, and is intended for students who completed the course on Data Science I. A distinctive feature of this course is the opportunity to apply data-science skills on a semester long project based on real-world data. After this course, students will be familiar with the most popular data science and machine learning techniques, ready to apply for a variety of data-science jobs in industry.
Upon completion of this course, students will be able to: conduct a data analysis project using an analytical programming language (R/Python); visualize and preprocess raw data in preparation for deeper forms of analysis; train a variety of machine learning models, including Decision Trees, Neural Networks, and Support Vector Machines; test and fine-tune analytic models to produce high accuracy rates; model evaluation will be achieved using cross-validation, learning and validation curves, grid search, and different performance metrics; use Ensemble Learning to improve model performance; avoid data overfitting by working on the Bias-Variance trade off.
For more information visit the course on Blackboard.
| Graded Work | Weight |
|---|---|
| Midterm Exams | 40% |
| Project Report: 1st Milestone | 20% |
| Project Report: 2nd Milestone | 20% |
| Project Report: 3rd Milestone | 20% |
| Dates to Remember | Event |
|---|---|
| January 17 | 1st class - check material on Blackboard |
| February 24 | 1st Milestone - Data Pre-processing |
| April 10 | 2nd Milestone - Data Modeling |
| May 5 | 3rd Milestone - Data Presentation and Visualization |
| March 2 | 1st Midterm Exam |
| April 20 | 2nd Midterm Exam |
Note: There is no final exam in this course.
| Dates | Topic |
|---|---|
| January 17, 19 | Overview of Statistical Learning |
| January 24, 26 | Linear Regression |
| January 31, February 2 | Classification |
| February 7, 9 | Resampling Methods |
| February 14, 16 | No Class |
| February 21, 23 | Linear Model Selection and Regularization |
| March 2 | 1st Midterm Exam |
| March 7, 9 | Nonlinear Regression |
| March 14, 16 | No Class; Spring Break |
| March 21, 23 | Tree-Based Methods |
| March 28, 30 | Ensemble Methods |
| April 4, 6 | Support Vector Machines |
| April 11, 13 | Unsupervised Learning |
| April 20 | 2nd Midterm Exam |
For more information visit the course on Blackboard.