Detection and Estimation (EE 60573)

University of Notre Dame


Spring 2027 - Enroll in EE 60573 section 01
place/data TBD

Course Vault, UNDER CONSTRUCTION

Description: This course studies methods for parameter estimation and signal detection found in engineering and learning system applications. The course covers three topics: model-based estimation theory, Baysian detection theory, and data-based learning of detection and regression models. Estimation theory topics include minimum variance and and minimum mean-squared estimation with applications in discrete-time Kalman filtering. Detection theory topicsinclude Bayesian, Neyman-Pearson, and minimax detectors with methods for bounding the detector error. Learning theory topics focus on bounding the generalization ability of neural network models using the probably-almost-correct (PAC) formalism.

Prerequisites:

  • Linear Systems (EE 60550), Probability and Random Processes (EE 60563)

Course Announcements:

  • TBA

Lecture Notes/Handouts:

  • Introduction to Detection and Estimation
  • Parameter Estimation Theory
  • Classical Detection Theory
  • Statistical Learning Theory

Reference Books:
  • Steven Kay, "Fundamentals of Statistical Signal Processing", (vol 1), Prentice Hall 1993.
  • T. Kailath, A.H. Sayed, B. Hassibi, "Linear Estimation", Prentice-Hall, 2000
  • K. Fukunaga, "Introduction to Statistical Pattern Recognition, Academic Press, 2nd edition, 1990)
  • V. Poor, "An Introduction to Signal Detection and Estimation, 2nd edition, Springer, 1994.
  • S. Shalev-Shwartz and S. Ben-David, "Understanding Machine Learning: from theory to algorithms", Cambridge, 2014
  • V.N. Vapnik, "Statistical Learning Theory", Wiley, 1998.
Grading: Homework 15% - 2 Midterm Exams 60% - Final Exam 25%
Instructor: Michael Lemmon, Dept. of Electrical Engineering, University of Notre Dame, Fitz 266 , lemmon at nd.edu