printer friendly pageCS 229B: Machine Learning for Sequence Modeling (STATS 232)
Sequence data and time series are becoming increasingly ubiquitous in fields as diverse as bioinformatics, neuroscience, health, environmental monitoring, finance, speech recognition/generation, video processing, and natural language processing. Machine learning has become an indispensable tool for analyzing such data; in fact, sequence models lie at the heart of recent progress in AI like GPT3. This class integrates foundational concepts in time series analysis with modern machine learning methods for sequence modeling. Connections and key differences will be highlighted, as well as how grounding modern neural network approaches with traditional interpretations can enable powerful leaps forward. You will learn theoretical fundamentals, but the focus will be on gaining practical, hands-on experience with modern methods through real-world case studies. You will walk away with a broad and deep perspective of sequence modeling and key ways in which such data are not just 1D images.
Terms: Aut
| Units: 3-4
Instructors:
Fox, E. (PI)
CS 229M: Machine Learning Theory (STATS 214)
How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering this question. This course will cover fundamental concepts and principled algorithms in machine learning, particularly those that are related to modern large-scale non-linear models. The topics include concentration inequalities, generalization bounds via uniform convergence, non-convex optimization, implicit regularization effect in deep learning, and unsupervised learning and domain adaptations. Prerequisites: linear algebra (
MATH 51 or
CS 205), probability theory (
STATS 116,
MATH 151 or
CS 109), and machine learning (
CS 229,
STATS 229, or
STATS 315A).
Terms: Aut
| Units: 3
CS 229S: Systems for Machine Learning
Deep learning and neural networks are being increasingly adopted across industries. They are now used to serve billions of users across applications such as search, knowledge discovery, and productivity assistants. As models become more capable and intelligent, this trend of large-scale adoption will continue to grow rapidly. Due to the widespread application, there is an increasing need to achieve high performance for both training and serving deep-learning models. However, performance is hindered by a multitude of infrastructure and lifecycle hurdles - the increasing complexity of the models, massive sizes of training and inference data, heterogeneity of the available accelerators and multi-node platforms, and diverse network properties. The slow adaptation of systems to new algorithms creates a bottleneck for the rapid evolution of deep-learning models and their applications. This course will cover systems approaches for improving the efficiency of machine learning pipelines - comprising data preparation, model training, and model deployment & inference -at each level of the systems stack spanning software and hardware.
Terms: Aut
| Units: 3
Instructors:
Arora, S. (PI)
;
Mirhoseini, A. (PI)
;
Kumbong, H. (TA)
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Instructors:
Arora, S. (PI)
;
Mirhoseini, A. (PI)
;
Kumbong, H. (TA)
;
Santhanam, K. (TA)
;
Spector, B. (TA)
CS 236: Deep Generative Models
Generative models are widely used in many subfields of AI and Machine Learning. Recent advances in parameterizing these models using neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech. In this course, we will study the probabilistic foundations and learning algorithms for deep generative models, including Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and flow models. The course will also discuss application areas that have benefitted from deep generative models, including computer vision, speech and natural language processing, and reinforcement learning. Prerequisites: Basic knowledge about machine learning from at least one of
CS 221, 228, 229 or 230. Students will work with computational and mathematical models and should have a basic knowledge of probabilities and calculus. Proficiency in some programming language, preferably Python, required.
Terms: Aut
| Units: 3
Instructors:
Ermon, S. (PI)
;
Agarwal, P. (TA)
;
Cao, S. (TA)
;
Chatterjee, S. (TA)
;
Chen, H. (TA)
;
Chiang, B. (TA)
;
Dodhia, P. (TA)
;
He, J. (TA)
;
Miraoui, Y. (TA)
;
Obbad, E. (TA)
;
Salahi, K. (TA)
;
Sharma, D. (TA)
;
Shih, A. (TA)
;
Xiao, Z. (TA)
;
Xu, M. (TA)
;
Yang, C. (TA)
;
Yuan, S. (TA)
;
Zhou, L. (TA)
CS 237A: Principles of Robot Autonomy I (AA 274A, EE 260A)
Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites:
CS 106A or equivalent,
CME 100 or equivalent (for linear algebra), and
CME 106 or equivalent (for probability theory).
Terms: Aut
| Units: 3
Instructors:
Schwager, M. (PI)
;
Agha-Oko, M. (TA)
;
Manhas, A. (TA)
...
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Instructors:
Schwager, M. (PI)
;
Agha-Oko, M. (TA)
;
Manhas, A. (TA)
;
Nagami, K. (TA)
;
Singh, S. (TA)
;
Vincent, J. (TA)
;
Yu, J. (TA)
CS 238: Decision Making under Uncertainty (AA 228)
This course is designed to increase awareness and appreciation for why uncertainty matters, particularly for aerospace applications. Introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Topics include: Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes. Applications cover: air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration. Prerequisites: basic probability and fluency in a high-level programming language.
Terms: Aut
| Units: 3-4
CS 242: Programming Languages
This course explores foundational models of computation, such as the lambda calculus and other small calculi, and the incorporation of basic advances in PL theory into modern programming languages such as Haskell and Rust. Topics include type systems (polymorphism, algebraic data types, static vs. dynamic), control flow (exceptions, continuations), concurrency/parallelism, metaprogramming, verification, and the semantic gap between computational models and modern hardware. The study of programming languages is equal parts systems and theory, looking at how a rigorous understanding of the semantics of computation enables formal reasoning about the behavior and properties of complex real-world systems. Prerequisites: 103, 110.
Terms: Aut
| Units: 3-4
CS 247A: Design for Artificial Intelligence (SYMSYS 195A)
A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247A is design for human-centered artificial intelligence experiences. What does it mean to design for AI? What is HAI? How do you create responsible, ethical, human centered experiences? Let us explore what AI actually is and the constraints, opportunities and specialized processes necessary to create AI systems that work effectively for the humans involved. Prerequisites: CS147 or equivalent background in design thinking. In the event of a waitlist, acceptance to class based on an application provided on the first day of class.
Terms: Aut
| Units: 3-4
Instructors:
Stanford, J. (PI)
;
Nabi, F. (TA)
CS 248B: Fundamentals of Computer Graphics: Animation and Simulation
This course provides a comprehensive introduction to computer graphics, focusing on fundamental concepts and techniques in Computer Animation and Physics Simulation. Topics include numerical integration, 3D character modeling, keyframe animation, skinning/rigging, inverse kinematics, rigid body dynamics, deformable body simulation, and fluid simulation. Prerequisites: CS107 and
MATH51.
Terms: Aut
| Units: 3
CS 251: Cryptocurrencies and blockchain technologies
For advanced undergraduates and for graduate students. The potential applications for Bitcoin-like technologies is enormous. The course will cover the technical aspects of cryptocurrencies, blockchain technologies, and distributed consensus. Students will learn how these systems work, and how to engineer secure software that interacts with Blockchains like Bitcoin, Ethereum, and others. Prerequisite:
CS110. Recommended:
CS255.
Terms: Aut
| Units: 3
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