printer friendly pageCS 102: Big Data: Tools and Techniques, Discoveries and Pitfalls
Aimed at non-CS undergraduate and graduate students who want to learn the basics of big data tools and techniques and apply that knowledge in their areas of study. Many of the world's biggest discoveries and decisions in science, technology, business, medicine, politics, and society as a whole, are now being made on the basis of analyzing massive data sets. At the same time, it is surprisingly easy to make errors or come to false conclusions from data analysis alone. This course provides a broad and practical introduction to big data: data analysis techniques including databases, data mining, and machine learning; data analysis tools including spreadsheets, relational databases and SQL, Python, and R; data visualization techniques and tools; pitfalls in data collection and analysis; historical context, privacy, and other ethical issues. Tools and techniques are hands-on but at a cursory level, providing a basis for future exploration and application. Prerequisites: comfort with basic logic and mathematical concepts, along with high school AP computer science,
CS106A, or other equivalent programming experience.
Terms: Aut, Spr
| Units: 3-4
| UG Reqs: WAY-AQR
Instructors:
Widom, J. (PI)
;
Chen, S. (TA)
;
Haigh, A. (TA)
;
Jang, J. (TA)
;
Kilpatrick, J. (TA)
;
Kunna, A. (TA)
;
Meister, C. (TA)
;
Min, J. (TA)
;
Pinkerton, R. (TA)
;
Shen, K. (TA)
;
Wang, L. (TA)
CS 231N: Convolutional Neural Networks for Visual Recognition
Computer Vision has become ubiquitous in our society, with applications innsearch, image understanding, apps, mapping, medicine, drones, andnself-driving cars. Core to many of these applications are the tasks of image classification, localization and detection. This course is a deep dive into details of neural network architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week 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. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge:
http://image-net.org/challenges/LSVRC/2014/index. Prerequisites: Proficiency in Python; familiarity with C/C++;
CS 131 and
CS 229 or equivalents;
Math 21 or equivalent, linear algebra.
Terms: Spr
| Units: 3-4
Instructors:
Johnson, J. (PI)
;
Li, F. (PI)
;
Yeung, S. (PI)
;
Asawa, C. (TA)
;
Beal, J. (TA)
;
Chen, V. (TA)
;
Chou, E. (TA)
;
Clow, J. (TA)
;
Dhar, M. (TA)
;
Fan, J. (TA)
;
Fu, A. (TA)
;
Garzon, P. (TA)
;
Guo, M. (TA)
;
Haque, A. (TA)
;
Huang, J. (TA)
;
Khandwala, N. (TA)
;
Kim, C. (TA)
;
Lin, P. (TA)
;
Liu, B. (TA)
;
Liu, X. (TA)
;
Mandlekar, A. (TA)
;
Peddada, A. (TA)
;
Roberts, M. (TA)
;
Sharma, P. (TA)
;
Xia, F. (TA)
;
Xu, D. (TA)
;
Zhang, B. (TA)
CS 275: Translational Bioinformatics (BIOE 217, BIOMEDIN 217, GENE 217)
Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies and course project. Prerequisites: programming ability at the level of
CS 106A and familiarity with biology and statistics.
Terms: Win
| Units: 4
CS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOMEDIN 279, BIOPHYS 279, CME 279)
Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellular-level simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. Prerequisites: elementary programming background (
CS 106A or equivalent) and an introductory course in biology or biochemistry.
Terms: Aut
| Units: 3
Instructors:
Dror, R. (PI)
;
Bedi, R. (TA)
;
El-Gabalawy, O. (TA)
;
Fernandes, D. (TA)
;
Paggi, J. (TA)
;
Sanborn, A. (TA)
CS 309A: Cloud Computing Seminar
For science, engineering, computer science, business, education, medicine, and law students. Cloud computing is bringing information systems out of the back office and making it core to the entire economy. Furthermore with the advent of smarter machines cloud computing will be integral to building a more precision planet. This class is intended for all students who want to begin to understand the implications of this technology. Guest industry experts are public company CEOs who are either delivering cloud services or using cloud services to transform their businesses.
Terms: Aut
| Units: 1
| Repeatable
for credit
Instructors:
Chou, T. (PI)
CS 371: Computational Biology in Four Dimensions (BIOMEDIN 371, BIOPHYS 371, CME 371)
Cutting-edge research on computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules, cells, and everything in between. These techniques, which draw on approaches ranging from physics-based simulation to machine learning, play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course is devoted primarily to reading, presentation, discussion, and critique of papers describing important recent research developments. Prerequisite:
CS 106A or equivalent, and an introductory course in biology or biochemistry. Recommended: some experience in mathematical modeling (does not need to be a formal course).
Terms: Win
| Units: 3
Instructors:
Dror, R. (PI)
CS 571: Surgical Robotics Seminar (ME 571)
Surgical robots developed and implemented clinically on varying scales. Seminar goal is to expose students from engineering, medicine, and business to guest lecturers from academia and industry. Engineering and clinical aspects connected to design and use of surgical robots, varying in degree of complexity and procedural role. May be repeated for credit.
Last offered: Autumn 2016
| Repeatable
for credit
CSRE 20N: What counts as "race," and why? (SOC 20N)
Preference to freshmen. Seminar discussion of how various institutions in U.S. society employ racial categories, and how race is studied and conceptualized across disciplines. Course introduces perspectives from demography, history, law, genetics, sociology, psychology, and medicine. Students will read original social science research, learn to collect and analyze data from in-depth interviews, and use library resources to conduct legal/archival case studies.
Terms: Win
| Units: 4
| UG Reqs: WAY-EDP, WAY-SI
Instructors:
Saperstein, A. (PI)
CSRE 122F: Histories of Race in Science and Medicine at Home and Abroad (AFRICAAM 122F, AFRICAST 122F, HISTORY 248D)
This course has as its primary objective, the historical study of the intersection of race, science and medicine in the US and abroad with an emphasis on Africa and its Diasporas in the US. By drawing on literature from history, science and technology studies, sociology and other related disciplines, the course will consider the sociological and cultural concept of race and its usefulness as an analytical category. The course will explore how the study of race became its own ¿science¿ in the late-Enlightenment era, the history of eugenics--a science of race aimed at the ostensible betterment of the overall population through the systematic killing or "letting die" of humanity¿s "undesirable" parts, discuss how the ideology of pseudo-scientific racism underpinned the health policies of the French and British Empires in Africa, explore the fraught relationship between race and medicine in the US, discuss how biological notions of race have quietly slipped back into scientific projects in the 21st century and explore how various social justice advocates and scholars have resisted the scientific racisms of the present and future and/or proposed new paths towards a more equitable and accessible science.
Terms: Spr
| Units: 4
Instructors:
Hill, R. (PI)
CSRE 123B: Literature and Human Experimentation (AFRICAAM 223, COMPLIT 223, HUMBIO 175H, MED 220)
This course introduces students to the ways literature has been used to think through the ethics of human subjects research and experimental medicine. We will focus primarily on readings that imaginatively revisit experiments conducted on vulnerable populations: namely groups placed at risk by their classification according to perceived human and cultural differences. We will begin with Mary Shelley's Frankenstein (1818), and continue our study via later works of fiction, drama and literary journalism, including Toni Morrison's Beloved, David Feldshuh's Miss Evers Boys, Hannah Arendt's Eichmann and Vivien Spitz's Doctors from Hell, Rebecca Skloot's Immortal Life of Henrietta Lacks, and Kazuo Ishiguro's Never Let Me Go. Each literary reading will be paired with medical, philosophical and policy writings of the period; and our ultimate goal will be to understand modes of ethics deliberation that are possible via creative uses of the imagination, and literature's place in a history of ethical thinking about humane research and care. Note: This course must be taken for a letter grade to be eligible for WAYS credit.
Terms: Spr
| Units: 3-5
| UG Reqs: WAY-A-II, GER:DB-Hum, GER:EC-EthicReas, WAY-ER
Instructors:
Ikoku, A. (PI)
;
Jayasinghe, D. (TA)
Filter Results: