Immersive Education in Virtual Reality and Artificial Intelligence
Acquire practical competencies in VR development, neural network implementation, and immersive technology design through instructor-led modules and applied projects.
Build deployable systems using current frameworks and validated methodologies recognized in professional practice across multiple industries.
Technical Foundation
Study core principles of spatial computing, neural architecture design, and real-time rendering pipelines through hands-on implementation tasks.
Complete individual assignments that require integration of VR hardware protocols with machine learning inference engines.
Develop working prototypes that demonstrate understanding of both theoretical frameworks and practical constraints in deployment environments.
- Spatial audio implementation and propagation modeling
- Interaction design for gesture-based interfaces
- Performance optimization for real-time rendering
- User testing protocols for immersive experiences
- Asset pipeline configuration and workflow automation
- Convolutional architecture for object recognition
- Recurrent models for temporal sequence prediction
- Transfer learning with pre-trained model adaptation
- Inference optimization for edge deployment
- Dataset preparation and augmentation strategies
- Requirements analysis for commercial applications
- Architecture design with scalability considerations
- Implementation of intelligent agent behaviors
- Integration testing with hardware peripherals
- Documentation and deployment preparation
Duration and Format
Program extends across sixteen weeks with scheduled live sessions twice weekly and asynchronous project work between meetings.
Each module requires approximately eighteen hours of work including lectures, laboratory exercises, and independent research.
Final assessment involves presentation of a functional prototype demonstrating integration of VR interfaces with AI-driven decision systems.
Program Instructors
Elliot Brennan
VR Systems Architect
Designed immersive training simulations for aerospace applications and led implementation of haptic feedback systems in industrial contexts.
Ingrid Solberg
Machine Learning Engineer
Developed predictive models for autonomous navigation and optimized neural architectures for real-time inference on embedded platforms.
Leif Dahlström
Technical Director
Managed deployment of distributed XR platforms and established development standards for cross-platform compatibility in enterprise environments.