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1 - 2 of 2 results for: CS224N

CS 224N: Natural Language Processing with Deep Learning (LINGUIST 284)

Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. Prerequisites: calculus and linear algebra; CS124 or CS121/221.
Terms: Win | Units: 3-4
Instructors: Manning, C. (PI)

CS 335: Fair, Accountable, and Transparent (FAT) Deep Learning

Deep learning-based AI systems have demonstrated remarkable learning capabilities. A growing field in deep learning research focuses on improving the Fairness, Accountability, and Transparency (FAT) of a model in addition to its performance. Although FAT will be difficult to achieve, emerging technical approaches in this topic show promise in making better FAT AI systems. In this course, we will study the rigorous computer science necessary for FAT deep learning and dive into the technical underpinnings of topics including fairness, robustness, interpretability, common sense, AI deception, and privacy. These topics reflect state-of-the-art research in FAT, are socially important, and they have strong industrial interest due to government and other policy regulation. This course will focus on the algorithmic and statistical methods needed to approach FAT AI from a deep learning perspective. We will also discuss several application areas where we can apply these techniques. Prerequisites more »
Deep learning-based AI systems have demonstrated remarkable learning capabilities. A growing field in deep learning research focuses on improving the Fairness, Accountability, and Transparency (FAT) of a model in addition to its performance. Although FAT will be difficult to achieve, emerging technical approaches in this topic show promise in making better FAT AI systems. In this course, we will study the rigorous computer science necessary for FAT deep learning and dive into the technical underpinnings of topics including fairness, robustness, interpretability, common sense, AI deception, and privacy. These topics reflect state-of-the-art research in FAT, are socially important, and they have strong industrial interest due to government and other policy regulation. This course will focus on the algorithmic and statistical methods needed to approach FAT AI from a deep learning perspective. We will also discuss several application areas where we can apply these techniques. Prerequisites: Intermediate knowledge of statistics, machine learning, and AI. Qualified students will have taken any one of the following, or their advanced equivalents: CS224N, CS230, CS231N, CS236, CS273B. Alternatively, students who have taken CS229 or have equivalent knowledge can be admitted with the permission of the instructors.
Terms: Spr | Units: 3
Instructors: Landay, J. (PI)
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