HKU Moodle

There are many emerging yet interesting parts assisting how human beings visualize and interact with the digital world, including display systems, optics & electronics, IMUs and sensors, real-time rendering, tracking, haptics, multimodal human perception and depth perception, stereo rendering, presence. This course’s emphasis on VR/AR/MR technology. Course Goals: This is a technical class. Students will learn about all hardware (optics, electronics, display, microcontroller, …) and software (JavaScript, WebGL, Python) aspects of Virtual and Augmented Reality (VR/AR).

The goal for this class is to learn all of these aspects in a hands-on manner. Each assignment is a small piece of a bigger project. The goal for each student or small team of students is to build a fully functional head mounted display, including optics, display, IMU, rendering, lens distortion shader, model loader etc., from off-the-shelf parts.

Artificial intelligence (AI) lies at the heart of next-generation wireless networks (e.g., 5G/6G). On the one hand, machine learning enables the design and optimization of increasingly dynamic and heterogeneous wireless networks. On the other hand, wireless networks are envisioned to serve as an integrated communication and computing platform to empower pervasive AI services at the edge.  To keep pace with this significant development trend, this course aims to provide a systematic and comprehensive guide to machine learning, wireless networks, and their vital interplay. It introduces students to the fundamentals of machine learning and wireless networks, followed by advanced machine learning algorithms for wireless networks and the design of wireless networks to support machine learning services.

Covered Topics:

Fundamentals of wireless networks (main content)

• Cellular systems (Concepts and design fundamentals, mobility management, resource allocation ...)
• WiFi (Medium Access Control …)
• Other emerging wireless networks

Fundamentals of machine learning (about 1 – 2 weeks)

• Supervised, reinforcement learning

Machine learning for/on wireless networks (about 1 week)

• Supervised/reinforcement learning to optimize wireless networks
• Edge computing/intelligence