261 - 270 of 380 results for: CS

Applying Machine Learning (ML) to solve real problems accurately and robustly requires more than just training the latest ML model. First, you will learn practical techniques to deal with data. This matters since real data is often not independently and identically distributed. It includes detecting covariate, concept, and label shifts, and modeling dependent random variables such as the ones in time series and graphs. Next, you will learn how to efficiently train ML models, such as tuning hyper-parameters, model combination, and transfer learning. Last, you will learn about fairness and model explainability, and how to efficiently deploy models. This class will teach both statistics, algorithms and code implementations. Homeworks and the final project emphasize solving real problems. Prerequisites: Python programing and machine learning ( CS 229), basic statistics. Please view course website here: https://c.d2l.ai/stanford- cs329p/

The goal of this practicum is to integrate methods from natural language processing with social psychological perspectives on race to build practical systems that address significant societal issues. Readings will be drawn broadly from across the social sciences and computer science. Students will work with large, complex datasets and participate in research involving community partnerships relevant to race and natural language processing. Prerequisite: CS224N, PSYCH290, or equivalent background in natural language processing. Students interested in participating should complete the online application for permission at https://web.stanford.edu/class/cs329r/. Limited enrollment.

This project-based course covers the iterative process for designing, developing, and deploying machine learning systems. It focuses on systems that require massive datasets and compute resources, such as large neural networks. Students will learn about data management, data engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects. In the process, students will learn about important issues including privacy, fairness, and security. Pre-requisites: At least one of the following; CS229, CS230, CS231N, CS224N or equivalent. Students should have a good understanding of machine learning algorithms and should be familiar with at least one framework such as TensorFlow, PyTorch, JAX.

This course will provide an introduction to state-of-the-art ML methods designed to make AI more trustworthy. The course focuses on four concepts: explanations, fairness, privacy, and robustness. We first discuss how to explain and interpret ML model outputs and inner workings. Then, we examine how bias and unfairness can arise in ML models and learn strategies to mitigate this problem. Next, we look at differential privacy and membership inference in the context of models leaking sensitive information when they are not supposed to. Finally, we look at adversarial attacks and methods for imparting robustness against adversarial manipulation.Students will gain understanding of a set of methods and tools for deploying transparent, ethically sound, and robust machine learning solutions. Students will complete labs, homework assignments, and discuss weekly readings. Prerequisites: CS229 or similar introductory Python-based ML class; knowledge of deep learning such as CS230, CS231N; familiarity with ML frameworks in Python (scikit-learn, Keras) assumed.

Recent advances in natural language processing (NLP), especially around large pretrained models, have enabled extensive successful applications. However, there are growing concerns about the negative aspects of NLP systems, such as biases and a lack of input from users. This course gives an overview of human-centered techniques and applications for NLP, ranging from human-centered design thinking to human-in-the-loop algorithms, fairness, and accessibility. Along the way, we will cover machine-learning techniques which are especially relevant to NLP and to human experiences. Prerequisite: CS224N or CS224U, or equivalent background in natural language processing. Prerequisite: CS224N or CS224U, or equivalent background in natural language processing.

While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively. This includes: goal-conditioned reinforcement learning techniques that leverage the structure of the provided goal space to learn many tasks significantly faster; meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly; curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer. This is a graduate-level course. By the end of the course, students should be able to understand and implement the state-of-the-art multi-task learning algorithms and be ready to conduct research on these topics. Prerequisites: CS 229 or equivalent. Familiarity with deep learning, reinforcement learning, and machine learning is assumed.

This course provides a research survey of advanced methods for robot learning in simulation, analyzing the simulation techniques and recent research results enabled by advances in physics and virtual sensing simulation. The course covers two main components: agent-environment interactions and domains for multi-agent and human-robot interaction. First, we cover agent-environment interactions by studying novel simulation environments for robotics, imitation and reinforcement learning methods, simulation for navigation and manipulation and `sim2real' techniques. In the second part, we explore models and algorithms for simulation and robot learning in multi-agent domains and human-robot interaction, studying the principles of learning for interactive tasks in which each agent collaborates to accomplish tasks. The topics include domains of social navigation, human-robot collaborative manipulation and multi-agent settings.nnThis a project-based seminar class. Projects will leverage the state-of-the-art simulation environment iGibson, in which students will develop simulations to explore learning and planning methods for diverse domains. We will provide a list of suggested projects but students might also propose an original idea. The course will cover a set of research papers with presentations by students. This is a research field in rapid transformation with exciting research lines. The goal of the class is to provide practical experience and understanding of the main research lines to enable students to conduct innovative research in this field.

This class will provide a core overview of essential topics and new research frontiers in reinforcement learning. Planned topics include: model free and model based reinforcement learning, policy search, Monte Carlo Tree Search planning methods, off policy evaluation, exploration, imitation learning, temporal abstraction/hierarchical approaches, safety and risk sensitivity, human-in-the-loop RL, inverse reinforcement learning, learning to communicate, and insights from human learning. Students are expected to create an original research paper on a related topic. Prerequisites: CS 221 or AA 238/ CS 238 or CS 234 or CS 229 or similar experience.

AI agents need to collaborate and interact with humans in many different settings such as bots operating on social media and crowdsourcing platforms, AI assistants brokering transactions on electronic marketplaces, autonomous vehicles driving alongside humans, or robots interacting with and assisting humans in homes. Our goal in this class is to learn about and design algorithms that enable robots and AI agents to reason about their actions, interact with one another, the humans, and the environment they live in, as well as plan safe strategies that humans can trust and rely on. This is a project-based graduate course that studies algorithms in robotics, machine learning, and control theory, which can improve the state-of-the-art human-AI systems. nnRecommended: Introductory course in AI ( CS 221) and Machine Learning ( CS 229).