IS 3432 Applied Deep Learning 3.0 Credits
This course covers the basic theory of deep learning in data science applications, preparing students to work with established deep learning methods to apply them to data science problems in different disciplines. Emphasizes practical, application-driven learning. Students will implement models incurrent frameworks, work with real datasets in areas like text, images, audio, social media, and user behavior analytics, and deepen experience with data preprocessing, feature engineering, and responsible model deployment. Throughout the course, students complete a series of programming assignments and support a team project that applies deep learning to a data science relevant domain.
Repeat Status: Not repeatable for credit
Prerequisites: MATH 1201 [Min Grade: D], MATH 1002 [Min Grade: D], MATH 2401 [Min Grade: D], MATH 2901 [Min Grade: D] (Can be taken Concurrently) or (CS 1020 [Min Grade: D] or CS 1030 [Min Grade: D] or IS 1551 [Min Grade: D] or ECE 1210 [Min Grade: D] or CS 171 [Min Grade: D] or INFO 151 [Min Grade: D] or ECE 105 [Min Grade: D]) or MATH 121 [Min Grade: D] or MATH 102 [Min Grade: D]) or MATH 201 [Min Grade: D] or MATH 261 [Min Grade: D]) and (MATH 2801 [Min Grade: D] or STAT 2201 [Min Grade: D] or PHYS 4801 [Min Grade: D] or MATH 1801 [Min Grade: D] or ECE 3700 [Min Grade: D] or BMES 2110 [Min Grade: D] or MATH 311 [Min Grade: D] or STAT 202 [Min Grade: D] or PHYS 440 [Min Grade: D] or MATH 410 [Min Grade: D] or ECE 361 [Min Grade: D] or BMES 310 [Min Grade: D])