Instructor:
Ricardo Vilalta (r.vilalta.us@ieee.org)
Office:
Durga D. and Sushila Agrawal Engineering Research Bldg. Room 203C.
Office Hours:
By appointment.
Class Time:
The class is fully online (asynchronous).
Telephone:
(713) 743-3614
Ricardo Vilalta (r.vilalta.us@ieee.org)
Durga D. and Sushila Agrawal Engineering Research Bldg. Room 203C.
By appointment.
The class is fully online (asynchronous).
(713) 743-3614
| Yasmin Farzana | Michail Koumpanakis |
|---|---|
| Office Hours: Thursdays 3:00 - 4:00 PM via MS Teams | Office Hours: Tuesdays 10:00 AM - Noon via MS Teams |
| Email: fyasmin2@uh.edu | Email: mkoumpanakis@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 variousdata-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); to visualize and preprocess raw data in preparation for deeper forms of analysis; to train a variety of machine learning models, including Decision Trees, Neural Networks, and Support Vector Machines; to test and fine-tune analytic models to produce high accuracy rates; to model evaluation will be achieved using cross-validation, learning and validation curves, grid search, and different performance metrics; to use Ensemble Learning to improve model performance; to avoid data overfitting by working on the Bias-Variance trade-off.
For more information, visit the course on Canvas.
| 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 16 | 1st class - check material on Blackboard |
| February 21 | 1st Milestone - Data Pre-processing |
| April 12 | 2nd Milestone - Data Modeling |
| May 3 | 3rd Milestone - Data Presentation and Visualization |
| February 28 | 1st Midterm Exam |
| April 17 | 2nd Midterm Exam |
Note: This course has no final exam.
| Dates | Topic |
|---|---|
| Week of January 16 | Overview of Statistical Learning |
| Week of January 22 | Linear Regression |
| Week of January 29 | Classification |
| Week of February 5 | Resampling Methods |
| Week of February 12 | No Class |
| Week of February 19 | Linear Model Selection and Regularization |
| February 28 | 1st Midterm Exam |
| Week of March 4 | Nonlinear Regression |
| Week of March 11 | No Class; Spring Break |
| Week of March 18 | Tree-Based Methods |
| Week of March 25 | Ensemble Methods |
| Week of April 1 | Support Vector Machines |
| Week of April 8 | Unsupervised Learning |
| April 17 | 2nd Midterm Exam |
For more information, visit the course on Canvas.