Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence provides advanced graduate preparation in Artificial Intelligence. This field covers machine learning, deep learning, large models, intelligent systems, and AI engineering applications, developing data modeling, algorithm implementation, system integration, and responsible AI capability.
Program Positioning
The master's program emphasizes advanced coursework, research methods, focused projects, and the ability to solve complex professional problems. This field covers machine learning, deep learning, large models, intelligent systems, and AI engineering applications, developing data modeling, algorithm implementation, system integration, and responsible AI capability.
Machine Learning Foundations
Study supervised learning, unsupervised learning, feature engineering, model training, and performance evaluation.
Deep Learning and Large Models
Understand neural networks, deep learning frameworks, natural language processing, computer vision, and large-model applications.
Data and Intelligent Systems
Practice data collection, cleaning, annotation, knowledge representation, intelligent application design, and system integration.
AI Engineering and Ethics
Develop skills in model deployment, testing, monitoring, privacy protection, algorithmic bias, and responsible AI practice.
Learning Objectives
Study and Completion Requirements
- Complete advanced professional coursework, research-methods training, and topical seminars.
- Conduct project research, case research, engineering analysis, or management analysis around field-specific problems.
- Complete a master's thesis, research report, or integrated outcome that meets program requirements.