printer friendly pageCS 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
Instructors:
Anari, N. (PI)
;
Charikar, M. (PI)
;
Sadigh, D. (PI)
;
Bhateja, C. (TA)
;
Fan, C. (TA)
;
Frausto, J. (TA)
;
Hejna, J. (TA)
;
Kim, J. (TA)
;
Lee, A. (TA)
;
Lin, C. (TA)
;
Mirchandani, S. (TA)
;
Obbad, E. (TA)
;
Salahi, K. (TA)
;
So, J. (TA)
;
Yang, S. (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:
Hashimoto, T. (PI)
;
Yang, D. (PI)
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
Instructors:
Maas, A. (PI)
CS 236G: Generative Adversarial Networks
Generative Adversarial Networks (GANs) have rapidly emerged as the state-of-the-art technique in realistic image generation. This course presents theoretical intuition and practical knowledge on GANs, from their simplest to their state-of-the-art forms. Their benefits and applications span realistic image editing that is omnipresent in popular app filters, enabling tumor classification under low data schemes in medicine, and visualizing realistic scenarios of climate change destruction. This course also examines key challenges of GANs today, including reliable evaluation, inherent biases, and training stability. After this course, students should be familiar with GANs and the broader generative models and machine learning contexts in which these models are situated. Prerequisites: linear algebra, statistics,
CS106B, plus a graduate-level AI course such as:
CS230,
CS229 (or
CS129), or
CS221.
Last offered: Winter 2022
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:
Hashimoto, T. (PI)
;
Yang, D. (PI)
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: Spring 2024
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:
Hashimoto, T. (PI)
;
Yang, D. (PI)
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