Master of Science in Artificial Intelligence (MSAI)
The Master of Science in Artificial Intelligence (MSAI) program at Houston Christian University cultivates expertise in machine learning, neural networks and intelligent systems through a comprehensive 33-credit curriculum. The AI world is ever-changing. New theoretical, technical and ethical challenges arise at every turn.
HCU’s innovative program integrates AI theory with ethical principles rooted in Christian tradition. The program combines this with hands-on application, preparing the next generation of AI innovators to shape landscape of this emerging technology for years to come.
HCU’s Unique Ethical Stewardship Training
What sets HCU’s MSAI 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 program 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 program culminates in either a research-focused thesis Capstone Project or an industry-based Applied Learning Practicum.
This is a low-residency program combining online learning with on-campus experiences. Each course includes a weekend (Friday-Saturday) residency held at the HCU campus, where students attend two-day intensive sessions for hands-on learning and collaboration.
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 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.
MS in Artificial Intelligence Degree Plan
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)
Expanding Career Opportunities in Artificial Intelligence
The AI field continues experiencing unprecedented growth, with increasing demand for specialists across industries. HCU’s MSAI graduates emerge prepared for diverse roles in the technology sector.
Career Paths and Roles
- Research Scientist: Pushes boundaries in machine learning and neural network development at leading technology companies and research institutions.
- AI Engineer: Implements sophisticated systems in enterprise environments, developing solutions for complex business challenges.
- Machine Learning Engineer: Designs and implements machine learning models and AI systems, bridging the gap between data science and software engineering.
- AI Ethics Consultant: Helps organizations navigate AI complexities while ensuring responsible development practices.
- Computer Vision Specialist: Develops next-generation surveillance and safety systems, working on image recognition and object detection.
- Natural Language Processing Expert: Advances communication technology and human-computer interaction systems.
- AI Architect: Designs and manages enterprise-level AI infrastructure and systems integration.
- Robotics Engineer: Designs, builds and maintains autonomous systems and robotic solutions.
- AI Product Manager: Guides the development of AI-powered products and solutions.
- AI Research Director: Leads teams in advancing new AI technologies and applications.
Industry Applications
AI expertise can have transformative impact on a variety of sectors, including healthcare through diagnostic systems and personalized medicine, finance through risk assessment and algorithmic trading, manufacturing through process optimization and quality control, energy through smart grid management, government through public service enhancement and transportation through logistics optimization.
International Student Internship Opportunities
The program includes off-campus internship opportunities for international students through the Applied Learning Practicum. Industry partnerships provide networking opportunities and exposure to real-world AI applications through guest lectures, workshops and collaborative projects.
Transform the Future with Artificial Intelligence
The Master of Science in Artificial Intelligence at Houston Christian University represents more than technical education – it’s an opportunity to shape the future of technology with wisdom and integrity. By combining advanced technical training with ethical principles rooted in Christian tradition, graduates emerge prepared to lead AI innovation responsibly across industries.
Program Contact
For more information about the MS in Computer and Information Sciences program, contact:
College of Science and Engineering
Houston Christian University
COSE@hc.edu
