271 - 280 of 380 results for: CS

Language models serve as the cornerstone of modern natural language processing (NLP) applications and open up a new paradigm of having a single general purpose system address a range of downstream tasks. As the field of artificial intelligence (AI), machine learning (ML), and NLP continues to grow, possessing a deep understanding of language models becomes essential for scientists and engineers alike. This course is designed to provide students with a comprehensive understanding of language models by walking them through the entire process of developing their own. Drawing inspiration from operating systems courses that create an entire operating system from scratch, we will lead students through every aspect of language model creation, including data collection and cleansing for pre-training, transformer model construction, model training, and evaluation before deployment. Application required, apply at https://docs.google.com/forms/d/e/1FAIpQLSdW0HgT8MHzdM8cgapLWqX2ZPP1yHSX52R_r5JzF52poqXsHg/viewform

AI has been advancing quickly, with its impact everywhere. In healthcare, innovation in AI could help transforming of our healthcare system. This course offers a diverse set of research projects focusing on cutting edge computer vision and machine learning technologies to solve some of healthcare's most important problems. The teaching team and teaching assistants will work closely with students on research projects in this area. Research projects include Care for Senior at Senior Home, Surgical Quality Analysis, AI Assisted Parenting, Burn Analysis & Assessment and more. AI areas include Video Understanding, Image Classification, Object Detection, Segmentation, Action Recognition, Deep Learning, Reinforcement Learning, HCI and more. The course is open to students in both school of medicine and school of engineering.

Understanding the human side of AI/ML based systems requires understanding both how the system-side AI works, but also how people think about, understand, and use AI tools and systems. This course will cover how what AI components and systems currently exits, along with how mental models and user models are made. These models lead to user expectations of AI systems are formed, and ultimately to design guidelines to avoid disappointing end-users by creating unintelligible AI tools that are based on a cryptic depiction of how things work. We'll also cover the ethics of AI data collection and model building, as well as how to build fair systems.

With modern high-density electrodes and optical imaging techniques, neuroscientists routinely measure the activity of hundreds, if not thousands, of cells simultaneously. Coupled with high-resolution behavioral measurements, genetic sequencing, and connectomics, these datasets offer unprecedented opportunities to learn how neural circuits function. This course will study statistical machine learning methods for analysing such datasets, including: spike sorting, calcium deconvolution, and voltage smoothing techniques for extracting relevant signals from raw data; markerless tracking methods for estimating animal pose in behavioral videos; network models for connectomics and fMRI data; state space models for analysis of high-dimensional neural and behavioral time-series; point process models of neural spike trains; and deep learning methods for neural encoding and decoding. We will develop the theory behind these models and algorithms and then apply them to real datasets in the homeworks and final project.This course is similar to STATS215: Statistical Models in Biology and STATS366: Modern Statistics for Modern Biology, but it is specifically focused on statistical machine learning methods for neuroscience data. Prerequisites: Students should be comfortable with basic probability ( STATS 116) and statistics (at the level of STATS 200). This course will place a heavy emphasis on implementing models and algorithms, so coding proficiency is required.

This course focuses on how robots can be effective teammates with other robots and human partners. Concepts and tools will be reviewed for characterizing task objectives, robot perception and control, teammate behavioral modeling, inter-agent communication, and team consensus. We will consider the application of these tools to robot collaborators, wearable robotics, and the latest applications in the relevant literature. This will be a project-based graduate course, with the implementation of algorithms in either python or C++.

Topics vary every quarter, and may include advanced material being taught for the first time. May be repeated for credit.

This is an implementation-heavy, lab-based class that continues the topics from CS240LX. The labs will be more specialized, with an emphasis on research-worthy topics and techniques. The class format will follow CS240LX: two labs, twice a week, along with a set of research papers for context. Enrollment requires instructor permission.

Language shapes thought; for 40 years, software systems and some of their research challenges have been defined by the C language. In the past 5 years, this has begun to change, with new languages (Rust, Go, coq) becoming competitors to C in large classes of systems. CS340R is a project-centric course that examines how the Rust programming language changes software systems, solving some problems while presenting new ones. This course seeks to ask and start to answer a simple question: "What are the most important open research challenges for software systems written in Rust?"