Course Websites

BIOE 488 - Applied High-Performance Computing for Imaging Science

Last offered Fall 2024

Official Description

Will introduce students to basic principles and practical applications of scientific computing as they relate to problems in machine learning and computed imaging. In this self-contained course, students will be introduced to a variety of important topics that underlie real-world machine learning and biomedical image computing tasks that are not typically comprised in a single course. The material will be presented in a practical way that will be accessible to engineering students who have a moderate level of experience in scientific computing but lack specific training in computer science. The emphasis will be on immediate applicability of scientific computing techniques as opposed to theoretical knowledge. The course will begin with an overview of good scientific coding practices in Python and introductions to canonical computing architectures. Subsequently, parallel computing concepts will be surveyed that address multi-core CPU and GPU-enabled systems. Distributed GPU computing on

Related Faculty

Subject Area

  • Bioengineering

Course Description

Will introduce students to basic principles and practical applications of scientific computing as they relate to problems in machine learning and computed imaging. In this self-contained course, students will be introduced to a variety of important topics that underlie real-world machine learning and biomedical image computing tasks that are not typically comprised in a single course. The material will be presented in a practical way that will be accessible to engineering students who have a moderate level of experience in scientific computing but lack specific training in computer science. The emphasis will be on immediate applicability of scientific computing techniques as opposed to theoretical knowledge. The course will begin with an overview of good scientific coding practices in Python and introductions to canonical computing architectures. Subsequently, parallel computing concepts will be surveyed that address multi-core CPU and GPU-enabled systems. Distributed GPU computing on a cluster will also be covered. The salient aspects of TensorFlow and/or other relevant machine learning programming environments will be introduced and utilized in applications of machine learning.

Credit Hours

3 hours

Prerequisites

Familiarity with the Python programming language. Restricted to students with senior undergraduate or graduate standing in an engineering major.

TitleSectionCRNTypeHoursTimesDaysLocationInstructor
Applied Hi-Per Comp Image SciACI77416LEC31400 - 1520 M W  139 Loomis Laboratory Matias Carrasco Kind
Applied Hi-Per Comp Image SciACO79876ONL3 -    Matias Carrasco Kind