printer friendly pageCS 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 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
Instructors:
Reingold, O. (PI)
;
Zou, J. (PI)
CS 230: Deep Learning
Deep Learning is one of the most highly sought after skills in AI. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. AI is transforming multiple industries. After this course, you will likely find creative ways to apply it to your work. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come in to class for advanced discussions and work on projects. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications).
CS 229 may be taken concurrently.
Terms: Aut, Win, Spr
| Units: 3-4
| UG Reqs: WAY-AQR, WAY-FR
Instructors:
Katanforoosh, K. (PI)
;
Ng, A. (PI)
;
Braatz, J. (TA)
...
more instructors for CS 230 »
Instructors:
Katanforoosh, K. (PI)
;
Ng, A. (PI)
;
Braatz, J. (TA)
;
Chuang, J. (TA)
;
Ciresi, S. (TA)
;
Desai, S. (TA)
;
Kosaraju, V. (TA)
;
Li, J. (TA)
;
Mao, H. (TA)
;
Mehr, L. (TA)
;
Mousavi, S. (TA)
;
Pal, A. (TA)
;
Rajpurkar, P. (TA)
;
Ruan, S. (TA)
;
Smith, C. (TA)
;
Waites, C. (TA)
;
Yang, J. (TA)
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: 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
CS 375: Large-Scale Neural Network Modeling for Neuroscience (PSYCH 249)
Introduction to designing, building, and training large-scale neural networks for modeling brain and behavioral data, including: deep convolutional neural network models of sensory systems (vision, audition, somatosensation); variational and generative methods for neural interpretation; recurrent neural networks for dynamics, memory and attention; interactive agent-based deep reinforcement learning for cognitive modeling; and methods and metrics for comparing such models to real-world neural data. Attention will be given both to established methods as well as cutting-edge techniques. Students will learn conceptual bases for deep neural network models and will also implement learn to implement and train large-scale models in Tensorflow using GPUs. Requirements: Fluency in Unix shell and Python programming; familiarity with differential equations, linear algebra, and probability theory; priori experience with modern machine learning concepts (e.g.
CS229) and basic neural network training tools (eg. CS230 and/or
CS231n). Prior knowledge of basic cognitive science or neuroscience not required but helpful.
Terms: Aut
| Units: 1-3
Instructors:
Yamins, D. (PI)
PSYCH 249: Large-Scale Neural Network Modeling for Neuroscience (CS 375)
Introduction to designing, building, and training large-scale neural networks for modeling brain and behavioral data, including: deep convolutional neural network models of sensory systems (vision, audition, somatosensation); variational and generative methods for neural interpretation; recurrent neural networks for dynamics, memory and attention; interactive agent-based deep reinforcement learning for cognitive modeling; and methods and metrics for comparing such models to real-world neural data. Attention will be given both to established methods as well as cutting-edge techniques. Students will learn conceptual bases for deep neural network models and will also implement learn to implement and train large-scale models in Tensorflow using GPUs. Requirements: Fluency in Unix shell and Python programming; familiarity with differential equations, linear algebra, and probability theory; priori experience with modern machine learning concepts (e.g.
CS229) and basic neural network training tools (eg. CS230 and/or
CS231n). Prior knowledge of basic cognitive science or neuroscience not required but helpful.
Terms: Aut
| Units: 1-3
Instructors:
Yamins, D. (PI)
Filter Results: