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1 - 5 of 5 results for: CS221

CS 81SI: AI Interpretability and Fairness

As black-box AI models grow increasingly relevant in human-centric applications, explainability and fairness becomes increasingly necessary for trust in adopting AI models. This seminar class introduces students to major problems in AI explainability and fairness, and explores key state-of-theart methods. Key technical topics include surrogate methods, feature visualization, network dissection, adversarial debiasing, and fairness metrics. There will be a survey of recent legal and policy trends. Each week a guest lecturer from AI research, industry, and related policy fields will present an open problem and solution, followed by a roundtable discussion with the class. Students have the opportunity to present a topic of interestnor application to their own projects (solo or in teams) in the final class. Code examples of each topic will be provided for students interested in a particular topic, but there will be no required coding components. Students who will benefit most from this clas more »
As black-box AI models grow increasingly relevant in human-centric applications, explainability and fairness becomes increasingly necessary for trust in adopting AI models. This seminar class introduces students to major problems in AI explainability and fairness, and explores key state-of-theart methods. Key technical topics include surrogate methods, feature visualization, network dissection, adversarial debiasing, and fairness metrics. There will be a survey of recent legal and policy trends. Each week a guest lecturer from AI research, industry, and related policy fields will present an open problem and solution, followed by a roundtable discussion with the class. Students have the opportunity to present a topic of interestnor application to their own projects (solo or in teams) in the final class. Code examples of each topic will be provided for students interested in a particular topic, but there will be no required coding components. Students who will benefit most from this class have exposure to AI, such as through projects and related coursework (e.g. statistics, CS221, CS230, CS229). Students who are pursuing subjects outside of the CS department (e.g. sciences, social sciences, humanities) with sufficient mathematical maturity are welcomed to apply. Enrollment limited to 20.
Terms: Spr | Units: 1

CS 221: Artificial Intelligence: Principles and Techniques

Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing,n Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 109, and CS 161 (algorithms, probability, and object-oriented programming in Python). We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review.
Terms: Aut, Spr | Units: 3-4

CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288)

Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Prerequisites: one of LINGUIST 180, CS 124, CS 224N, CS224S, or CS221.
Terms: Spr | Units: 3-4

LINGUIST 188: Natural Language Understanding (CS 224U, LINGUIST 288)

Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Prerequisites: one of LINGUIST 180, CS 124, CS 224N, CS224S, or CS221.
Terms: Spr | Units: 3-4

LINGUIST 288: Natural Language Understanding (CS 224U, LINGUIST 188)

Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Prerequisites: one of LINGUIST 180, CS 124, CS 224N, CS224S, or CS221.
Terms: Spr | Units: 3-4
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