AI and Data Science
The structure of the curriculum is as follows:
| I) | Required Courses | 24 units |
| II) | Elective Courses | 18 units |
| 42 units |
Requirements
| I. | Required Courses | 24 units |
| COMP1007 Introduction of Python and Its Applications | 3 units | |
| COMP1016 Mathematical Methods for Business Computing | 3 units | |
| COMP2016 Database Management | 3 units | |
| COMP3057 Introduction to Artificial Intelligence and Machine Learning | 3 units | |
| MATH1005 Calculus I | 3 units | |
| MATH1026 Probability and Statistics with Software | 3 units | |
| MATH1205 Discrete Mathematics | 3 units | |
| MATH2207 Linear Algebra I | 3 units | |
| II. | Elective Courses | 18 units |
| COMP3065 Artificial Intelligence Application Development | 3 units | |
| COMP3066 Health and Assistive Technology: Practicum | 3 units | |
| COMP3076 AI and Generative Arts | 3 units | |
| COMP3115 Exploratory Data Analysis and Visualization | 3 units | |
| COMP4125 Visual Analytics | 3 units | |
| COMP4026 Computer Vision and Pattern Recognition | 6 units | |
| COMP4045 Human-Computer Interaction | 6 units | |
| COMP4135 Recommender Systems and Applications | 3 units | |
| COMP4136 Natural Language Processing | 3 units | |
| MATH3206 Scientific Computing I | 3 units | |
| MATH3626 Computational Statistics for Data Science | 3 units | |
| MATH3805 Regression Analysis | 3 units | |
| MATH3807 Simulation | 3 units | |
| MATH3816 Statistical Analysis of Sample Surveys | 3 units | |
| MATH3836 Data Mining | 3 units | |
| MATH3845 Interest Theory and Applications | 3 units | |
| MATH4225 Foundation of Big Data and Learning | 3 units | |
| MATH4227 Programming for Data Science | 3 units | |
| MATH4826 Time Series and Forecasting | 3 units | |
| 42 units |
