This course provides an in-depth exploration of statistical learning and decision-making processes for ECE students. It covers fundamental concepts, principles and methods in classical statistical learning, machine learning, Markovian processes and decision theory. Students will gain a strong foundation in statistical learning theories, practical experience with machine learning and deep learning techniques, and the ability to apply Markovian processes to decision-making problems. Practical experience is emphasized through hands-on labs and project-based learning.

CILO-1: Recognize and interpret classical statistical methods and learning techniques. CILO-2: Design, implement, and evaluate machine learning and deep learning algorithms for various applications. CILO-3: Differentiate and model Markovian processes and implement decision theory in real-world scenarios. CILO-4: Use statistical learning and decision theory to solve practical problems.