Machine Learning & AI
Graduate Certificate
To obtain this certificate, a student must meet all prerequisites, and complete four total courses, two required courses and two elective courses.
Prerequisites:
- Enrolled as a 色色研究所 graduate student
Required courses:
Students must complete a sequence of two courses:
| Required | Credits |
|---|---|
| CSCI 6521: Advanced Machine Learning I | 3 |
| CSCI 6522: Advanced Machine Learning II | 3 |
Professional Skills Attained:
- CSCI 6521 Learn theory & applications for statistical models: Regression, Probability, Bayesian, Kernels.
- CSCI 6522 Learn to code machine learning models: Neural Networks and Applications
Electives: Choose Two
Students choose two courses from the list of seven options:
| Electives (Choose Two) | Credits |
|---|---|
| CSCI 6250: Big Data Analytics & Systems | 3 |
| CSCI 6454: Parallel & Scientific Computing | 3 |
| CSCI 6633: Computer Vision | 3 |
| CSCI 6634: Data Visualization | 3 |
| CSCI 6645: Planning Algorithms in Artificial Intelligence | 3 |
| CSCI 6650: Intelligent Agents and Multi-Agent Systems | 3 |
| CSCI 6990: Topics in Advanced Computer Science | 3 |
Professional Skills Attained:
- CSCI 6250 Learn techniques in Data Mining, Database Warehousing such as HADOOP, Map Reduce, HBase
- CSCI 6454 Learn techniques in processing large volumes of data in parallel
- CSCI 6633 Learn approaches to evaluate and analyze visual data for information
- CSCI 6634 Learn techniques & tools for graphically modeling visuals for complex datasets .
- CSCI 6645 Learn techniques for planning algorithms.
- CSCI 6650 Learn techniques for multiagent system, human to computer systems, computer to computer
- CSCI 6990 Learn recent advancements and new trends in the field. Note: Special topic must relate to ML/AI to receive credit towards certificate
Student Learning Outcomes
Student Learning Outcomes specify what students will know, be able to do, or be able to demonstrate when they have completed a program of study.