printer friendly pageCS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)
Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites:
CS106B
Terms: Aut
| Units: 3-4
| UG Reqs: WAY-AQR
Instructors:
Jurafsky, D. (PI)
;
Cruz, D. (TA)
;
Kim, M. (TA)
;
Lowber, A. (TA)
;
Lu, H. (TA)
;
Newman, B. (TA)
;
Ocampo, P. (TA)
;
Qushair, Y. (TA)
;
Soylu, F. (TA)
;
Sun, A. (TA)
;
Wade, B. (TA)
;
Yu, D. (TA)
CS 224N: Natural Language Processing with Deep Learning (LINGUIST 284, SYMSYS 195N)
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,
CS221, or
CS229.
Terms: Win
| Units: 3-4
Instructors:
Manning, C. (PI)
;
Ali, K. (TA)
;
Banerjee, G. (TA)
;
Chi, E. (TA)
;
Goldie, A. (TA)
;
Hong, F. (TA)
;
Kanodia, S. (TA)
;
Lam, G. (TA)
;
Li, V. (TA)
;
Mitchell, E. (TA)
;
Newman, B. (TA)
;
Rai, M. (TA)
;
Shen, K. (TA)
;
Sui, E. (TA)
;
Sun, A. (TA)
;
Wolff, C. (TA)
;
Yasunaga, M. (TA)
;
Yu, K. (TA)
;
Zhang, Y. (TA)
;
Zheng, L. (TA)
;
Zhou, A. (TA)
CS 224S: Spoken Language Processing (LINGUIST 285)
Introduction to spoken language technology with an emphasis on dialogue and conversational systems. Deep learning and other methods for automatic speech recognition, speech synthesis, affect detection, dialogue management, and applications to digital assistants and spoken language understanding systems. Prerequisites:
CS124,
CS221,
CS224N, or
CS229.
Terms: Spr
| Units: 2-4
LINGUIST 284: Natural Language Processing with Deep Learning (CS 224N, SYMSYS 195N)
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,
CS221, or
CS229.
Terms: Win
| Units: 3-4
Instructors:
Manning, C. (PI)
;
Ali, K. (TA)
;
Banerjee, G. (TA)
...
more instructors for LINGUIST 284 »
Instructors:
Manning, C. (PI)
;
Ali, K. (TA)
;
Banerjee, G. (TA)
;
Chi, E. (TA)
;
Goldie, A. (TA)
;
Hong, F. (TA)
;
Kanodia, S. (TA)
;
Lam, G. (TA)
;
Lamm, M. (TA)
;
Li, V. (TA)
;
Mitchell, E. (TA)
;
Newman, B. (TA)
;
Rai, M. (TA)
;
Shen, K. (TA)
;
Sui, E. (TA)
;
Sun, A. (TA)
;
Wolff, C. (TA)
;
Yasunaga, M. (TA)
;
Yu, K. (TA)
;
Zhang, Y. (TA)
;
Zheng, L. (TA)
;
Zhou, A. (TA)
LINGUIST 285: Spoken Language Processing (CS 224S)
Introduction to spoken language technology with an emphasis on dialogue and conversational systems. Deep learning and other methods for automatic speech recognition, speech synthesis, affect detection, dialogue management, and applications to digital assistants and spoken language understanding systems. Prerequisites:
CS124,
CS221,
CS224N, or
CS229.
Last offered: Winter 2021
SYMSYS 195N: Natural Language Processing with Deep Learning (CS 224N, 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,
CS221, or
CS229.
Terms: Win
| Units: 3-4
Instructors:
Manning, C. (PI)
;
Ali, K. (TA)
;
Banerjee, G. (TA)
...
more instructors for SYMSYS 195N »
Instructors:
Manning, C. (PI)
;
Ali, K. (TA)
;
Banerjee, G. (TA)
;
Chi, E. (TA)
;
Goldie, A. (TA)
;
Hong, F. (TA)
;
Kanodia, S. (TA)
;
Lam, G. (TA)
;
Li, V. (TA)
;
Mitchell, E. (TA)
;
Newman, B. (TA)
;
Rai, M. (TA)
;
Shen, K. (TA)
;
Sui, E. (TA)
;
Sun, A. (TA)
;
Wolff, C. (TA)
;
Yasunaga, M. (TA)
;
Yu, K. (TA)
;
Zhang, Y. (TA)
;
Zheng, L. (TA)
;
Zhou, A. (TA)
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