printer friendly pageCS 226: The Future of Mechanical Engineering (ME 228)
This seminar series provides an overview of current and emerging research topics in mechanical engineering and its application to engineering and scientific problems. The seminar is targeted at senior mechanical engineering undergraduates and mechanical engineering graduate students. Presenters will be selected external speakers who feature exciting and cutting-edge research of mechanical engineering.
Last offered: Winter 2023
CS 227A: Robot Perception: Hardware, Algorithm, and Application (EE 227)
Robot Perception is the cornerstone of modern robotics, enabling machines to interpret, understand, and respond to an array of sensory information they encounter. In the course, students will study the basic principles of typical sensor hardware on a robotics system (e.g., vision, tactile, and acoustic sensors), the algorithms that process the raw sensory data, and make actionable decisions from that information. Over the course of the semester, students will incrementally build their own vision-based robotics system in simulation via a series of homework coding assignments. Students enrolling 4 units will be required to submit an additional final written report. Prerequisites: This course requires programming experience in python as well as basic knowledge of linear algebra. Most of the required mathematical concepts will be reviewed, but it will be assumed that students have strong programming skills. All the homework requires extensive programming. Previous knowledge of robotics, machine learning or computer vision would be helpful but is not absolutely required.
Terms: Win
| Units: 3-4
Instructors:
Song, S. (PI)
;
Nie, N. (TA)
CS 227B: General Game Playing
A general game playing system accepts a formal description of a game to play it without human intervention or algorithms designed for specific games. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Students create GGP systems to compete with each other and in external competitions. Prerequisite: programming experience. Recommended: 103 or equivalent.
Terms: Spr
| Units: 3
Instructors:
Genesereth, M. (PI)
CS 228: Probabilistic Graphical Models: Principles and Techniques
Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning. Prerequisites: basic probability theory and algorithm design and analysis.
Terms: Win
| Units: 3-4
Instructors:
Ermon, S. (PI)
;
Marx, C. (TA)
;
Patel, C. (TA)
;
Sharma, D. (TA)
;
Thomas, G. (TA)
;
Zalouk, S. (TA)
CS 229: Machine Learning (STATS 229)
Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy to the equivalency of
CS106A,
CS106B, or
CS106X, familiarity with probability theory to the equivalency of
CS 109,
MATH151, or
STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or
CS205.
Terms: Aut, Win, Sum
| Units: 3-4
Instructors:
Amjad, J. (PI)
;
Charikar, M. (PI)
;
Fox, E. (PI)
;
Guestrin, C. (PI)
;
Koyejo, S. (PI)
;
Ng, A. (PI)
;
Agarwal, R. (TA)
;
Agarwala, S. (TA)
;
Chang, C. (TA)
;
Chi, R. (TA)
;
Chow, W. (TA)
;
Chu, S. (TA)
;
Damiani, A. (TA)
;
Deng, R. (TA)
;
Desai, R. (TA)
;
Ding, Z. (TA)
;
Dong, K. (TA)
;
Frausto, J. (TA)
;
Jeon, H. (TA)
;
Khandelwal, P. (TA)
;
Kumbong, H. (TA)
;
Schaeffer, R. (TA)
;
So, J. (TA)
;
Wang, A. (TA)
;
Wang, R. (TA)
;
Xiao, Z. (TA)
;
Yang, S. (TA)
;
Zhang, E. (TA)
CS 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 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.
Last offered: Spring 2023
| UG Reqs: WAY-AQR, WAY-FR
CS 231A: Computer Vision: From 3D Perception to 3D Reconstruction and Beyond
(Formerly 223B) An introduction to the concepts and applications in computer vision. Topics include: cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo, as well as high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization. Prerequisites: linear algebra, basic probability and statistics.
Terms: Spr
| Units: 3-4
Instructors:
Bohg, J. (PI)
;
Savarese, S. (PI)
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