printer friendly pageCOMPMED 299: Directed Reading in Comparative Medicine
Prerequisite: consent of instructor. (Staff)
Terms: Aut, Win, Spr, Sum
| Units: 1-18
| Repeatable
for credit
COMPMED 370: Medical Scholars Research
Provides an opportunity for student and faculty interaction, as well as academic credit and financial support, to medical students who undertake original research. Enrollment is limited to students with approved projects.
Terms: Aut, Win, Spr, Sum
| Units: 4-18
| Repeatable
for credit
COMPMED 399: Graduate Research
Investigations sponsored by individual faculty members.Opportunities are available in comparative medicine and pathology, immuno-histochemistry, electron microscopy, molecular genetics, quantitative morphometry, neuroanatomy and neurophysiology of the hippocampus, pathogenesis of intestinal infections, immunopathology, biology of laboratory rodents, anesthesiology of laboratory animals, gene therapy of animal models of neurodegenerative diseases, and development and characterization of transgenic animal models. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-18
| Repeatable
for credit
COMPMED 801: TGR Project
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
Instructors:
Albertelli, M. (PI)
;
Darian-Smith, C. (PI)
;
Felt, S. (PI)
...
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Instructors:
Albertelli, M. (PI)
;
Darian-Smith, C. (PI)
;
Felt, S. (PI)
;
Garner, J. (PI)
;
Hestrin, S. (PI)
CS 102: Big Data: Tools and Techniques, Discoveries and Pitfalls
Aimed primarily at students who may not major in CS but want to learn about big data 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, but it is surprisingly easy to come to false conclusions from data analysis alone, and privacy of data connected to individuals can be a major concern. This course provides a broad introduction to big data: historical context and case studies; privacy issues; data analysis techniques including databases, data mining, and machine learning; sampling and statistical significance; data analysis tools including spreadsheets, SQL, Python, R; data visualization techniques and tools. Tools and techniques are hands-on but at a cursory level, providing a basis for future exploration and application. Prerequisites: high school AP computer science,
CS106A, or other equivalent programming experience; comfort with statistics and spreadsheets helpful but not required.
Terms: Spr
| Units: 3-4
| UG Reqs: WAY-AQR
Instructors:
Chan, E. (PI)
;
Wang, L. (PI)
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
CS 275: Translational Bioinformatics (BIOE 217, BIOMEDIN 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
Instructors:
Gevaert, O. (PI)
;
Mallick, P. (PI)
;
Wall, D. (PI)
;
Gloudemans, M. (TA)
;
McInnes, G. (TA)
;
Shenoy, A. (TA)
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
CS 309A: Cloud Computing
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)
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