printer friendly pageBIODS 237: Deep Learning in Genomics and Biomedicine (BIOMEDIN 273B, CS 273B, GENE 236)
Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Exper
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Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Experts in the field will present guest lectures. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as
CS 109, and basic machine learning such as
CS 229. No prior knowledge of genomics is necessary.
Terms: Spr
| Units: 3
Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
BIOMEDIN 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, CS 273B, GENE 236)
Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Exper
more »
Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Experts in the field will present guest lectures. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as
CS 109, and basic machine learning such as
CS 229. No prior knowledge of genomics is necessary.
Last offered: Spring 2023
CME 107: Introduction to Machine Learning (EE 104)
Introduction to machine learning. Formulation of supervised and unsupervised learning problems. Regression and classification. Data standardization and feature engineering. Loss function selection and its effect on learning. Regularization and its role in controlling complexity. Validation and overfitting. Robustness to outliers. Simple numerical implementation. Experiments on data from a wide variety of engineering and other disciplines. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites:
ENGR 108;
EE 178 or
CS 109; CS106A or equivalent.
Terms: Spr
| Units: 3-5
Instructors:
Boyd, S. (PI)
;
Devanathan, N. (TA)
CS 109: Introduction to Probability for Computer Scientists
Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of
MATH 51 or
CME 100 or equivalent.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: WAY-AQR, WAY-FR, GER:DB-EngrAppSci
Instructors:
Cain, J. (PI)
;
Cochran, K. (PI)
;
Harvill, M. (PI)
;
Piech, C. (PI)
;
Alagbe, K. (TA)
;
Borbon Miranda, C. (TA)
;
Buch, D. (TA)
;
Chellah, O. (TA)
;
Cheng, K. (TA)
;
Cochran, K. (TA)
;
El Boudali, H. (TA)
;
Fakih, N. (TA)
;
Grover, K. (TA)
;
Gupta, A. (TA)
;
Harvill, M. (TA)
;
Kang, R. (TA)
;
Mejia, J. (TA)
;
Michel, I. (TA)
;
Palleti, R. (TA)
;
Qiu, T. (TA)
;
Ravi, N. (TA)
;
Tinker, J. (TA)
;
Vu, M. (TA)
;
Wang, S. (TA)
;
Woodrow, J. (TA)
;
Xie, M. (TA)
CS 109ACE: Problem-solving Lab for CS109
Additional problem solving practice for the introductory CS course
CS109. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Enrollment limited to 30 students, permission of instructor required. Concurrent enrollment in
CS 109 required.
Terms: Aut, Win, Spr
| Units: 1
Instructors:
Qin, M. (PI)
CS 161: Design and Analysis of Algorithms
Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 106B or 106X; 103 or 103B; 109 or
STATS 116.
Terms: Aut, Win, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors:
Anari, N. (PI)
;
Charikar, M. (PI)
;
Rubinstein, A. (PI)
...
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Instructors:
Anari, N. (PI)
;
Charikar, M. (PI)
;
Rubinstein, A. (PI)
;
Sotoudeh, M. (PI)
;
Conkey, A. (TA)
;
Cussen, H. (TA)
;
Dixit, A. (TA)
;
Garg, R. (TA)
;
Goyal, S. (TA)
;
Hosgur, E. (TA)
;
Ko, J. (TA)
;
Kolichala, P. (TA)
;
Li, W. (TA)
;
Liu, S. (TA)
;
Mayer, T. (TA)
;
Namboothiry, B. (TA)
;
Parada, R. (TA)
;
Patterson, K. (TA)
;
Rivkin, J. (TA)
;
Roghani, M. (TA)
;
Salahi, K. (TA)
;
Singh, A. (TA)
;
Singhal, A. (TA)
;
Stearns, C. (TA)
;
Wang, X. (TA)
;
Z. HaoChen, J. (TA)
;
Zhang, P. (TA)
;
Zhao, J. (TA)
CS 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)
;
Koyejo, S. (PI)
;
Liang, P. (PI)
;
Phatak, U. (PI)
;
Sadigh, D. (PI)
;
Agarwal, R. (TA)
;
Bruzzese, T. (TA)
;
Camacho, S. (TA)
;
Dixit, A. (TA)
;
Frausto, J. (TA)
;
Hejna, J. (TA)
;
Hu, G. (TA)
;
Jeon, H. (TA)
;
Kansal, A. (TA)
;
Khurana, A. (TA)
;
Ko, J. (TA)
;
Lee, A. (TA)
;
Li, C. (TA)
;
Lin, K. (TA)
;
Liu, S. (TA)
;
Melo, L. (TA)
;
Michaels, J. (TA)
;
Nie, N. (TA)
;
Pampari, A. (TA)
;
Pandya, D. (TA)
;
Roman, E. (TA)
;
Ryan, M. (TA)
;
Taori, R. (TA)
;
Tchapmi, M. (TA)
;
Tchapmi P., L. (TA)
;
Verma, S. (TA)
;
Wang, R. (TA)
;
Wornow, M. (TA)
;
Yang, S. (TA)
;
Zhang, P. (TA)
;
Zhang, T. (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 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 231N: Deep Learning for Computer Vision
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.Prerequisites: Proficiency in Python - All class assignments will be in Python (and use numpy) (we provide a tutorial here for those who
more »
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.Prerequisites: Proficiency in Python - All class assignments will be in Python (and use numpy) (we provide a tutorial here for those who aren't as familiar with Python). If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Javascript) you will probably be fine.College Calculus, Linear Algebra (e.g.
MATH 19,
MATH 51) -You should be comfortable taking derivatives and understanding matrix vector operations and notation. Basic Probability and Statistics (e.g.
CS 109 or other stats course) -You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc.
Terms: Spr
| Units: 3-4
Instructors:
Adeli, E. (PI)
;
Li, F. (PI)
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