This course focuses on how to address inference in complex engineering settings. While driven by applications, the course is not about these applications. Rather, it emphasizes a common foundation and conceptual framework for inference-related questions arising in various fields including, but not limited to, machine learning, signal processing, artificial intelligence, computer vision, control, and communication. We focus on inference: learning about the hidden state of the world that we care from available observations. We rely on the powerful language of probabilistic graphical models to leverage on the inherent structure of the given problem and efficiently perform inference. Graphical models build upon the beautiful marriage between probability theory and graph theory and use graphs to capture the fundamental structure of multivariate statistical models and also design efficient computation for inference tasks.
Prerequisites: Students entering the class should be comfortable with programming and should have a pre-existing working knowledge of linear algebra (e.g., MATH 308), probability and statistics (e.g., CSE 312/STAT390), and algorithms. For a brief refresher, we recommend that you consult the statistics/probability reference materials on the Textbooks page.
Grading: Your grade will be based exclusively on 5 homework assignments: HW0 (9%), HW1 (20%), HW2 (20%), HW3 (20%), HW4 (30%). There are no exams or credit given in any way other than the homeworks. 1% of the grade will be given to those who submit course evaluation and email a proof.