printer friendly pageCS 272: Introduction to Biomedical Data Science Research Methodology (BIOE 212, BIOMEDIN 212, GENE 212)
Capstone Biomedical Data Science experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites:
BIOMEDIN 210 or 214 or 215 or 217 or 260. Preference to BMI graduate students. Consent of instructor required.NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Spr
| Units: 3-5
CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 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)
CS 273C: Cloud Computing for Biology and Healthcare (BIOMEDIN 222, GENE 222)
Big Data is radically transforming healthcare. To provide real-time personalized healthcare, we need hardware and software solutions that can efficiently store and process large-scale biomedical datasets. In this class, students will learn the concepts of cloud computing and parallel systems' architecture. This class prepares students to understand how to design parallel programs for computationally intensive medical applications and how to run these applications on computing frameworks such as Cloud Computing and High Performance Computing (HPC) systems. Prerequisites: familiarity with programming in Python and R.
Terms: Spr
| Units: 3
CS 274: Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, GENE 214)
BIOMEDIN 214: Representations and Algorithms for Computational Molecular Biology (
BIOE 214,
CS 274,
GENE 214)Topics: This is a graduate level introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, BLAST, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, molecular dynamics and energy minimization, statistical analysis of 3D structure, knowledge controlled terminologies for molecular function, expression analysis, chemoinformatics, pharmacogenetics, network biology. Lectures are supplemented with assignments and programming projects, which allow students to implement important computational biology algorithms. Firm prerequisite:
CS 106B. NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Aut
| Units: 3-4
CS 275: Translational Bioinformatics (BIOE 217, BIOMEDIN 217, GENE 217)
Analytic and interpretive methods to optimize the transformation of genetic, genomic, and biological data into diagnostics and therapeutics for medicine. Topics: access and utility of publicly available data sources; types of genome-scale measurements in molecular biology and genomic medicine; linking genome-scale data to clinical data and phenotypes; and new questions in biomedicine using bioinformatics. Case studies. Prerequisites: programming ability at the level of
CS 106A and familiarity with statistics and biology.
Terms: Spr
| Units: 3-4
CS 275A: Symbolic Musical Information (MUSIC 253)
Properties of symbolic data for music applications including advanced notation systems, data durability, mark-up languages, optical music recognition, and data-translation tasks. Hands-on work involves these digital score formats: Guido Music Notation, Humdrum, MuseData, MEI, MusicXML, SCORE, and MIDI internal code.
Terms: Win
| Units: 2-4
Instructors:
Sapp, C. (PI)
;
Selfridge-Field, E. (PI)
CS 275B: Computational Music Analysis (MUSIC 254)
Leveraging off three synchronized sets of symbolic data resources for notation and analysis, the lab portion introduces students to the open-source Humdrum Toolkit for music representation and analysis. Issues of data content and quality as well as methods of information retrieval, visualization, and summarization are considered in class. Grading based primarily on student projects. Prerequisite: 253 or consent of instructor.
Terms: Spr
| Units: 2-4
Instructors:
Sapp, C. (PI)
;
Selfridge-Field, E. (PI)
CS 276: Information Retrieval and Web Search (LINGUIST 286)
Text information retrieval systems; efficient text indexing; Boolean, vector space, and probabilistic retrieval models; ranking and rank aggregation; evaluating IR systems; text clustering and classification; Web search engines including crawling and indexing, link-based algorithms, web metadata, and question answering; distributed word representations. Prerequisites:
CS 107,
CS 109,
CS 161.
Last offered: Spring 2019
CS 277: Foundation Models for Healthcare (BIODS 271, RAD 271)
Generative AI and large-scale self-supervised foundation models are poised to have a profound impact on human decision making across occupations. Healthcare is one such area where such models have the capacity to impact patients, clinicians, and other care providers. In this course, we will explore the training, evaluation, and deployment of generative AI and foundation models, with a focus on addressing current and future medical needs. The course will cover models used in natural language processing, computer vision, and multi-modal applications. We will explore the intersection of models trained on non-healthcare domains and their adaptation to domain-specific problems, as well as healthcare-specific foundation models. Prerequisites: Familiarity with machine learning principles at the level of
CS 229, 231N, or 224N
Terms: Win
| Units: 3
Instructors:
Chaudhari, A. (PI)
;
Syeda-Mahmood, T. (PI)
;
Zou, J. (PI)
...
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Instructors:
Chaudhari, A. (PI)
;
Syeda-Mahmood, T. (PI)
;
Zou, J. (PI)
;
Varma, M. (TA)
;
Wu, K. (TA)
CS 278: Social Computing (SOC 174, SOC 274)
Today we interact with our friends and enemies, our team partners and romantic partners, and our organizations and societies, all through computational systems. How do we design these social computing systems - platforms for social media, online communities, and collaboration - to be effective and responsible? This course covers design patterns for social computing systems and the foundational ideas that underpin them.
Terms: Spr
| Units: 3-4
Instructors:
Bernstein, M. (PI)
;
Doby, S. (TA)
;
Miller, G. (TA)
;
Phatak, U. (TA)
;
Rodriguez, S. (TA)
;
Verma, S. (TA)
;
Zhou, G. (TA)
;
Zuna Largo, W. (TA)
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