Special Topics Course

Machine learning on graphs (static/dynamic, attributed, undirected/directed, single/ensemble) has emerged as an important research topic that finds applications in many domains including social networks, infrastructure design and maintenance, drug discovery, brain networks, and material design. This course will discuss recent advances in machine learning on graphs including neural network architectures and methods to encode graphs into low-dimensional spaces to facilitate machine learning.

In this course, we will examine upcoming user interface technologies that will impact how we interact with our devices and digital content in the future. These include: immersive technologies (augmented and virtual reality), physiological interfaces (e.g., brain and body interfaces), wearable computing (e.g., devices both for reading and writing data to the user's body), multi-sensory and multimodal interactions in mixed, augmented and virtual realities (e.g., spatial audio, body movement), haptics (e.g., force feedback, sensing weight, feeling textures), and others.

This course covers advanced topics on information retrieval, web search, and related scalable information systems. The content to be focused includes search engines, indexing, retrieval, and ranking, and support for large-scale online search services/data processing. Recent papers in top conferences will be reviewed, and issues in relevance, efficiency, and scalability will be studied.

This course will focus on learning problems for networking, i.e., how network protocols or network operators make their decisions at different granularities (e.g., network, TCP, application, etc.) to keep networks safe and performant. How these decisions are made right now, and how one can replace existing heuristics-based decision-making with ML-based learning models. We will learn about the challenges of applying machine learning to learning problems in networking and how we can resolve them.

The Internet of Things (IoT) embeds ordinary physical objects in our environment with digital intelligence -- via sensing, control, communications, and compute capabilities. As such, IoT advances are likely key drivers of the next computing revolution.  This course will teach IoT systems end-to-end, including the fundamentals of IoT devices, software architectures, communication protocols, security approaches, as well as the distributed, multi-tier (e.g. sensors, edge, and cloud) programming and deployment considerations needed to support emerging IoT applications. 

This course provides an introduction to digital audio through the lens of the software used by the Allosphere Research Group at UC Santa Barbara. (See: https://github.com/AlloSphere-Research-Group ). We will learn the basics of music synthesis (e.g.

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 This is a graduate-level research topic course. We will first introduce the basics of quantum computing and cover the essential components of a quantum computer system, including quantum algorithm, quantum programming language, and quantum compiler. quantum computer architecture, quantum devices, etc. Theory and practice are combined in this course. The students will be able to understand and derive basic quantum algorithms by hand. They will present a research paper in class.

The commonly accepted idea of a Metaverse stems from science fiction, where it is portrayed as a shared and persistent virtual world. Since its introduction by Neal Stephenson in his 1992 novel Snow Crash, the Metaverse has been interpreted in a number of ways. In most imaginings of the Metaverse, virtual reality (VR) is the one technology that is most closely tied to it. This course will introduce you to the basics of VR with focus on learning to build VR systems. The course will cover the design of VR using the Unity game engine.

This is a theoretical/mathematical course studying randomized algorithms. The course will have a focus on Markov Chain Monte Carlo (MCMC) algorithms, and techniques for analyzing the convergence rate of MCMC algorithms. Interested students should have completed CMPSC 130B by the start of the course and should be comfortable with basic probability theory.

Prerequisite: CMPSC 130B (can be taking it in Winter quarter 2023).