Signal Prediction and Classification (SPC) course, 2026
Welcome to the Signal Prediction and Classification (SPC) course!
- This page concentrates on course information and materials.
- For communication and submissions, please refer to the complimentary Moodle page.
Course Overview
This course integrates data-driven machine learning (ML) with principles of digital signal processing (DSP) and random processes, primarily through the least squares (LS) method.
Course objectives:
By the end of this course, students will be able to:
- Evaluate signal characteristics including amplitude, frequency, and phase.
- Analyze signals using auto-correlation and partial auto-correlation functions.
- Utilize AR, MA, and ARMA models for signal prediction (regression).
- Apply ARMA models to multivariate exogenous and endogenous signals.
- Compare and select appropriate performance metrics.
- Classify signals using logistic regression and feature engineering techniques.
Special Notes:
- This is a newly developed course tailored to be self-contained, with no formal prerequisite courses required. However, a basic understanding of probability and linear algebra is assumed.
- The course emphasizes hands-on learning, with assignments conducted using agentic-AI.
Previous Exams