Artificial Intelligence
Artificial intelligence (AI) uses computers and technology to achieve the outcomes of human problem- solving and decision-making abilities. It comprises methods to solve easy problems for humans but hard for digital computers, such as Natural Language Understanding, Natural Language Generation, and Image Understanding.
This program is suitable for many people with a quantitative background or experience dealing with data and who would like to obtain some grounding in artificial intelligence. Examples include software engineers who want to complement their programming skills with Machine Learning and Deep Learning modeling skills, pharmaceutical data analysts who explore the transition from traditional statistical analytics to contemporary deep learning models, and many more.
List of the requirements for the AI certification program are as follows:
12 credits are required, which can be satisfied by:
o 2 core courses (6 credits)
o 2 elective courses (6 credits)
Admission Requirements
Applicants should have a bachelor's degree and have some experience with programming and data analytics. Some experience with programming, equivalent to CS 602 (Java), and some experience in data analytics, equivalent to CS 482 (Data Mining).
What are the Courses to be taken?
Students must take a total of four courses, with the following compulsory core courses and elective courses to choose from.
Core courses:
| Code | Title | Credits |
|---|---|---|
| CS 670 | Artificial Intelligence | 3 |
| DS 675 | Machine Learning | 3 |
Elective courses: Select one application track and choose two elective courses from that selected track.
| Code | Title | Credits |
|---|---|---|
| 1. Data Science | ||
| DS 633 | Advances in Representation Learning | 3 |
| DS 637 | Python and Mathematics for Machine Learning * | 3 |
| DS 669 | Reinforcement Learning | 3 |
| DS 677 | Deep Learning | 3 |
| DS 680 | Natural Language Processing | 3 |
| DS 681 | Deep Learning for Computer Vision | 3 |
| DS 683 | Graph Neural Networks | 3 |
| DS 685 | Artificial Intelligence for Robotics | 3 |
| DS 688 | Advanced Federated Machine Learning | 3 |
| DS 698 | Special Emerging Topics | 3 |
| DS 732 | Theoretical Foundation of Machine Learning | 3 |
| or CS 732 | Advanced Machine Learning | |
| DS 786 | Selected Topics in Data Science | 3 |
| DS 789 | Trustworthy Artificial Intelligence | 3 |
| 2. Computer Science | ||
| CS 634 | Data Mining | 3 |
| CS 659 | Image Processing and Analysis | 3 |
| CS 681 | Computer Vision | 3 |
| CS 782 | Pattern Recognition and Applications | 3 |
| 3. Statistics | ||
| MATH 663 | Introduction to Biostatistics | 3 |
| MATH 665 | Statistical Inference | 3 |
| MATH 678 | Statistical Methods in Data Science | 3 |
| 4. Robotics | ||
| ME 625 | Introduction to Robotics | 3 |
| BME 673 | Biorobotics | 3 |
| BME 676 | Computational Biomechanics | 3 |
| BME 760 | Modeling in Func Brain Imaging | 3 |
| 5. Engineering | ||
| ECE 744 | Optimization for Data Engineering | 3 |
| ECE 776 | Information Theory | 3 |
| BME 661 | Neural Engineering | 3 |
| MTEN 633 | Machine Learning for Chemical and Materials Engineers | 3 |
| CE 506 | Remote Sensing of Environment | 3 |
| CE 739 | Structural Optimization | 3 |
| 6. Science | ||
| BIOL 601 | Computational Biology I | 3 |
| BIOL 635 | Intro to Comp Neuroscience | 3 |
| BIOL 636 | Advanced Comp Neuroscience | 3 |
| BIOL 638 | Computational Ecology | 3 |
| BIOL 672 | Computational Systems Biology | 3 |
| CHEM 716 | Integrated Drug Dev & Discover | 3 |
| CHEM 737 | Applications of Computational Chemistry and Molecular Modeling | 3 |
| 7. Business and Management | ||
| FIN 616 | Data Driven Financial Modeling | 3 |
| ACCT 640 | Big Data Analytics for Accounting | 3 |
| MGMT 630 | Decision Analysis with Quantitative Modeling | 3 |
| MGMT 735 | Deep Learning in Business | 3 |
* DS 637 is recommended as an introductory course, offering a review of mathematics for machine learning to students with a limited background in mathematics or programming.
Sample course sequence:
1. CS 634, CS 670, DS 675, DS 677
2. DS 675, CS 670, DS 669, DS 680
3. CS 670, DS 675, DS 677, CS 681
