CAPSTONE PROJECT IN MACHINE LEARNING AND SIGNAL PROCESSING

Prerequisites & course information

Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Summer 2024 Learning Outcomes: 1. Identify a real-world problem that can be addressed and answered using techniques in machine learning and signal processing Audience: Graduate 2. Think critically about the end-to-end formulation of a real-world machine learning and signal processing task Audience: Graduate 3. Apply MLSP concepts to a real-world machine learning and signal processing task Audience: Graduate 4. Ask and answer deep and driving questions about a machine learning and signal processing project Audience: Graduate 5. Gain, sharpen, and showcase skills in teamwork, problem solving, reflection, and communication Audience: Graduate Learning Outcomes: 1. Identify a real-world problem that can be addressed and answered using techniques in machine learning and signal processing Audience: Graduate 2. Think critically about the end-to-end formulation of a real-world machine learning and signal processing task Audience: Graduate 3. Apply MLSP concepts to a real-world machine learning and signal processing task Audience: Graduate 4. Ask and answer deep and driving questions about a machine learning and signal processing project Audience: Graduate 5. Gain, sharpen, and showcase skills in teamwork, problem solving, reflection, and communication Audience: Graduate