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121 - 130 of 587 results for: Medicine

COMPMED 290: MLAS Career Development

Restricted to MLAS students. Focus is on career exploration for students interested in the field of laboratory animal care, animal facility management or animal welfare. Limited enrollment. Requires department approval to enroll. May be taken up to six quarters.
Terms: Aut, Win, Spr | Units: 1-6 | Repeatable for credit

COMPMED 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

CS 102: Big Data: Tools and Techniques, Discoveries and Pitfalls

Terms: Spr | Units: 3-4 | UG Reqs: WAY-AQR

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

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)
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