CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning
A survey of numerical approaches to the continuous mathematics used in computer vision and robotics with emphasis on machine and deep learning. Although motivated from the standpoint of machine learning, the course will focus on the underlying mathematical methods including computational linear algebra and optimization, as well as special topics such as automatic differentiation via backward propagation, momentum methods from ordinary differential equations, CNNs, RNNs, etc. (Replaces
CS205A, and satisfies all similar requirements.) Prerequisites:
Math 51;
Math 104 or 113 or equivalent or comfortable with the associated material.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Fedkiw, R. (PI)
CS 210A: Software Project Experience with Corporate Partners
Twoquarter project course. Focus is on realworld software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as startup companies with a budget and a technical advisory board comprised of instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; realworld software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites:
CS 109 and 110.
Terms: Win

Units: 34

Grading: Letter (ABCD/NP)
Instructors:
Borenstein, J. (PI)
CS 223A: Introduction to Robotics (ME 320)
Robotics foundations in modeling, design, planning, and control. Class covers relevant results from geometry, kinematics, statics, dynamics, motion planning, and control, providing the basic methodologies and tools in robotics research and applications. Concepts and models are illustrated through physical robot platforms, interactive robot simulations, and video segments relevant to historical research developments or to emerging application areas in the field. Recommended: matrix algebra.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Bohg, J. (PI)
;
Khatib, O. (PI)
CS 224N: Natural Language Processing with Deep Learning (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 largescale NLP problem. Prerequisites: calculus and linear algebra; CS124 or
CS121/221.
Terms: Win

Units: 34

Grading: Letter or Credit/No Credit
Instructors:
Manning, C. (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: 34

Grading: Letter or Credit/No Credit
Instructors:
Ermon, S. (PI)
CS 229A: Applied Machine Learning
You will learn to implement and apply machine learning algorithms. This course emphasizes practical skills, and focuses on giving you skills to make these algorithms work. You will learn about commonly used learning techniques including supervised learning algorithms (logistic regression, linear regression, SVM, neural networks/deep learning), unsupervised learning algorithms (kmeans), as well as learn about specific applications such as anomaly detection and building recommender systems. This class is taught in the flippedclassroom format. You will watch videos and complete indepth programming assignments and online quizzes at home, then come to class for discussion sections. This class will culminate in an openended final project, which the teaching team will help you on. Prerequisites: Programming at the level of CS106B or 106X, and basic linear algebra such as
Math 51.
Terms: Aut, Win, Spr

Units: 34

Grading: Letter or Credit/No Credit
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 flippedclassroom format. You will watch videos and complete indepth 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 openended 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: 34

Grading: Letter or Credit/No Credit
Instructors:
Katanforoosh, K. (PI)
;
Ng, A. (PI)
;
Bartolome Aramburu, C. (TA)
...
more instructors for CS 230 »
Instructors:
Katanforoosh, K. (PI)
;
Ng, A. (PI)
;
Bartolome Aramburu, C. (TA)
;
Cho, P. (TA)
;
Dery, L. (TA)
;
Eng, D. (TA)
;
Koochak, Z. (TA)
;
Momeni, A. (TA)
;
Shenoi, A. (TA)
;
Whang, J. (TA)
;
Yang, B. (TA)
CS 231A: Computer Vision: From 3D Reconstruction to Recognition
(Formerly 223B) An introduction to the concepts and applications in computer vision. Topics include: cameras and projection models, lowlevel image processing methods such as filtering and edge detection; midlevel vision topics such as segmentation and clustering; shape reconstruction from stereo, as well as highlevel vision tasks such as object recognition, scene recognition, face detection and human motion categorization. Prerequisites: linear algebra, basic probability and statistics.
Terms: Win

Units: 34

Grading: Letter or Credit/No Credit
CS 232: Digital Image Processing (EE 368)
Image sampling and quantization color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, noise reduction and restoration, feature extraction and recognition tasks, image registration. Emphasis is on the general principles of image processing. Students learn to apply material by implementing and investigating image processing algorithms in Matlab and optionally on Android mobile devices. Term project. Recommended:
EE261,
EE278.
Terms: Win

Units: 3

Grading: Letter (ABCD/NP)
Instructors:
Girod, B. (PI)
CS 234: Reinforcement Learning
To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will briefly cover background on Markov decision processes and reinforcement learning, before focusing on some of the central problems, including scaling up to large domains and the exploration challenge. One key tool for tackling complex RL domains is deep learning and this class will include at least one homework on deep reinforcement learning. Prerequisites: proficiency in python,
CS 229 or equivalents or permission of the instructor; linear algebra, basic probability.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Brunskill, E. (PI)
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