HCU’s Unique Ethical Stewardship Training
What sets HCU’s MS in Artificial Intelligence program apart is its unique fusion of technical expertise and ethical considerations, all viewed through a Christian perspective. In a landscape where AI ethics and responsible development are increasingly crucial, graduates emerge not only as skilled AI specialists but as responsible stewards of technology.
Robust, Cutting-Edge AI Curriculum
The MS in Artificial Intelligence consists of 33 credit hours of comprehensive coursework. Students must complete all courses with a grade of B or better while maintaining a minimum 3.0 cumulative GPA. The artificial intelligence degree online culminates in either a research-focused thesis Capstone Project or an industry-based Applied Learning Practicum.
Review degree plan
MS in Artificial Intelligence Courses
- Foundations of Artificial Intelligence: An introduction to the fundamental concepts, techniques and applications of artificial intelligence. This course covers problem-solving, knowledge representation, search algorithms and intelligent agents through lectures, discussions and hands-on exercises.
- Machine Learning: Explore the theory, algorithms and applications of machine learning techniques, including supervised learning, unsupervised learning and reinforcement learning. Students gain hands-on experience implementing machine learning algorithms and applying them to real-world datasets.
- Neural Networks and Deep Learning: Delve into the theory, architectures and applications of neural networks, including feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs) and deep belief networks (DBNs).
- Natural Language Processing: Master techniques for enabling computers to understand, interpret and generate human language. This course covers text preprocessing, syntactic and semantic analysis, sentiment analysis, machine translation and dialogue systems.
- Computer Vision: Engage with image processing, feature extraction, object detection, image segmentation and classification. Students gain practical experience designing and implementing computer vision systems for tasks such as image recognition, object tracking and scene understanding.
- Robotics: Study robot kinematics, dynamics, motion planning, perception and control. Through laboratory exercises and hands-on projects, students design and implement robotic systems for manipulation, navigation and human-robot interaction.
- Reinforcement Learning: Master algorithms for training agents to make sequential decisions in uncertain environments, including Q-learning, policy gradients and deep reinforcement learning techniques. Applies these methods to control tasks and games.
- Data Mining and Big Data Analytics: Focus on extracting knowledge from large and complex datasets using data preprocessing, pattern recognition, clustering, classification and association analysis. Students work with distributed computing frameworks and real-time processing methodologies.
- Evolutionary and Cognitive Computing: Explore bio-inspired computing methods including genetic algorithms, evolutionary strategies, swarm intelligence and neural-symbolic systems. Students design and implement evolutionary algorithms for optimization and pattern recognition tasks.
- AI Ethics and Society: Examine ethical frameworks, principles and guidelines for responsible AI development through case studies and current applications. This course explores bias and fairness, transparency and accountability, privacy and data protection, autonomy and control.
Capstone Project and Applied Learning Practicum
Students in the MS in Artificial Intelligence program complete their studies through either a research-focused thesis Capstone Project or an industry-based Applied Learning Practicum. The thesis option enables deeper exploration of emerging AI technologies, contributing new knowledge to the field. The practicum provides hands-on experience implementing AI solutions in enterprise environments.
Learning Modalities
This MS in Artificial Intelligence is offered as a fully online program or as a low-residency program. The artificial intelligence degree online takes place in dynamic, easy-to-use online learning environments, including nonsynchronous online coursework and learning projects.
The low-residency option combines online learning with on-campus experiences. Each course includes one residency weekend session (Friday-Saturday) held at the HCU campus, where you will be required to attend two-day intensive sessions for hands-on learning and collaboration.
Admissions Requirements
- Bachelor’s degree from a regionally accredited university with a minimum 3.0 GPA
- Undergraduate degrees in Computer Science, Mathematics, Electrical Engineering or similar fields are preferred, but not required
- Conditional admission consideration for GPAs between 2.5 and 2.99
- For International Students:
- Successful completion of the English Proficiency Test
- FTE (Foreign Transcript Evaluation of Bachelor’s degree transcript)
