Special Topics Course

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This is a graduate-level research course on Conversational AI.  Over the duration of this course, we will delve into the latest publications within the expansive domain of Large Language Models (LLMs) and Conversational AI.

This course will teach you everything about offline rendering, so you will be able to write a fully functional industry-level renderer (such as Disney's Hyperion and Pixar's RenderMan) that produces stunning graphics. Topics in this course will cover the physics of light, the rendering equation, Monte Carlo integration, path tracing, physically-based reflectance models, participating media, other advanced light transport methods, production rendering approaches, and so on.

There’s a lot of excitement about blockchains and cryptocurrencies mixed with a lot of skepticism and pessimism, but advances in the foundations underlying blockchains are undisputable. The goal of this course is to weave an overview of prominent blockchains systems with key technical advances the field has instigated.

Course purpose: The course focuses on the ability to translate theoretical knowledge about algorithms, data structures, and complexity to efficiently be able to perform the complete process of analyzing a problem, estimate the running time of suggested solutions, and to implement a running program.

In Fall 2023 I will teach a course on Parameterized Algorithms (Similar to the course taught in Spring 2019) - we will (likely) cover chapters 1-7, 13 and 14 of the Parameterized Algorithms book, plus possibly some more recent research papers.

This course offers students an introduction to some of the latest state-of-the-art techniques in the field of deep learning. Throughout the course, students will delve into a wide range of advanced topics and methodologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative models such as large language models, and transformers, among others.

This course explores advanced topics in highly scalable Internet services and their underlying systems’ architecture. Software today is primarily delivered as a service: accessible globally via web browsers and mobile applications, and backed by millions of servers. Modern web frameworks (e.g., Ruby on Rails, Django, and Express), and continuous improvements to cloud providers (e.g., Amazon Web Services, Google Cloud Platform, and Microsoft Azure) make it increasingly easier to build and deploy these systems.

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.