Stanford commences the Natural language Understanding course CS224u. The course can be taken entirely online and asynchronously. The course which existed before, has been made free by the university and is now accessible and open to all. The core content for the course is delivered through slides, YouTube videos, and Python notebooks. The classes will be a mix of special events recorded and working with support from the teaching team. The university has made the content available in a self-paced version and to anyone for free. 

The grades for the course are determined by a series of short quizzes, event participation, homework and bake-offs, literature review, experimental protocol, and final project paper. The university wants the students to pursue unified interdisciplinary projects that weave themes from multiple classes together. On the other hand, they also need to ensure that the final projects for this course are original and involve substantial new efforts, for which they are adopting a policy on joint submission. 

NLU is a subset of NLP. It deals with machine reading comprehension. NLU is used in products like voice-driven assistants, natural-language search, question answering, statement analysis of automated trading, business intelligence, social media analytics, and content summarizations. Its implementation includes Amazon Alexa, Google Assistant, Cortana and Siri. In this Stanford course, students will learn how to develop systems and algorithms for the robust machine understanding of human language. It will specifically look into lexical semantics, distributed representations of meaning, relaxation extraction, semantic parsing, sentiment analysis, and dialogue agents. 

The online course is an adaptation of the spring 2021 course taught on campus. The course commences with the distributed word representation models and then moves on to relation extraction, natural language inference and grounded language understanding. 

Earlier, Stanford conducted a graduate-level one-semester course that served Natural Language Processing with Deep Learning. The primary focus of the study was on investigating the fundamental concepts and ideas in NLP under a Deep Learning approach. It looked at the understanding of both the algorithms available for processing linguistic information and the underlying computational properties of the natural languages.