Academics / Courses / DescriptionsCOMP_SCI 396, 496: Machine Learning and Sensing
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Prerequisites
PhDs in CS, TSB, CE or EE or Permission by Instructor. The course expects students to have a background in python programming.Description
Modern technology relies heavily on the ability to process and understand sensor data through machine learning. Whether it's a smartphone interpreting touch gestures, smart speakers recognizing voice commands, or autonomous vehicles perceiving their environment, the challenge lies in extracting reliable information from complex sensor inputs. This course examines this critical intersection, teaching students how to build intelligent sensing systems from the ground up. Through practical assignments and hands-on projects, students will learn the complete pipeline: collecting sensor data, data and signal pre-processing, feature engineering, developing machine learning models, and visualizing results. The course emphasizes real-world applications through interactive demonstrations, in class tutorials and collaborative project work, with special focus on cutting-edge deep learning techniques for processing multimodal sensor data. Students will develop their skills through technical assignments and a team project, preparing them to develop next-generation sensing applications (Syllabus).
REFERENCE TEXTBOOKS: N/AREQUIRED TEXTBOOK: N/A
COURSE COORDINATORS: Prof. Ahuja
COURSE INSTRUCTOR: Prof. Ahuja (Spring)