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1 - 6 of 6 results for: BIODS ; Currently searching offered courses. You can also include unoffered courses

BIODS 215: Topics in Biomedical Data Science: Large-scale inference

The recent explosion of data generated in the fields of biology and medicine has led to many analytical challenges and opportunities for understanding human health. This graduate-level course focuses on methodology for large-scale inference from biomedical data. Topics include one-dimensional and multidimensional probability distributions; hypothesis testing and model comparison; statistical modeling; and prediction. This course will place a special emphasis on applications of these approaches to i) human genetic data; ii) hospital in-patient and health questionnaire data, which is increasingly available with the emergence of large precision initiatives like the UK Biobank and Precision Medicine Initiative; and iii) wearable and social network data.
Terms: Win | Units: 2-3 | Grading: Medical Option (Med-Ltr-CR/NC)

BIODS 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. Stude 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. 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 CS109, and basic machine learning such as CS229. No prior knowledge of genomics is necessary.
Terms: Aut | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)

BIODS 260A: Workshop in Biostatistics (STATS 260A)

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Terms: Aut | Units: 1-2 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

BIODS 260B: Workshop in Biostatistics (STATS 260B)

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Terms: Win | Units: 1-2 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

BIODS 260C: Workshop in Biostatistics (STATS 260C)

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Terms: Spr | Units: 1-2 | Repeatable for credit | Grading: Medical Satisfactory/No Credit

BIODS 299: Directed Reading and Research

For students wishing to receive credit for directed reading or research time. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit | Grading: Medical Option (Med-Ltr-CR/NC)
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