Schedule

Below, you can find an overview of all lectures and tutorials with topics and required reading. An overview of the schedule with rooms can also be found on TimeEdit.

Lecture & Tutorial Plan

Session Time Topics Reading
Lecture 1 August 31, 13–15
  • Introduction and overview of the course.
  • Model-based versus data-driven inference.
  • Introduction to detection theory.
  • Orthodox approach to detection: Neyman-Pearson theorem, probability of detection and false alarm, and receiver operating characteristic (ROC).
  • Bayesian approach to detection: Bayesian cost. Probability of error.
  • Kay-II Chapter 1, Sections 3.1-3.7
Lecture 2 September 02, 08–10
  • Detection of deterministic vector signals in white Gaussian noise.
  • Dealing with colored noise. Pre-whitening.
  • Matched filter.
  • Kay-II Sections 4.1-4.4, 4.6
Lecture 3 September 04, 13–15
  • Detection of random Gaussian signals in Gaussian noise.
  • Optimal detector and its performance.
  • Special cases and examples.
  • Kay-II Sections 5.1-5.4, 5.6-5.7
Lecture 4 September 07, 13–15
  • Contrasting the orthodox and Bayesian approach.
  • Detection with M>2 hypotheses.
  • Deterministic vector signals in white Gaussian noise.
  • Special cases and examples.
  • Kay-II Sections 3.8, 4.5, 4.7
Tutorial 1 September 07, 15–17
  • Walk-through by the instructor
    • Monte-Carlo simulation
    • 2020-10-26 (Problem 1)
  • Problems from Kay-II
    • 1.2, 3.4, 3.6, 3.14, 4.6, 4.8, 4.15, 4.16, 4.19
  • Problems from the "additional problems" document
    • Number 1
    • Number 2
Lecture 5 September 09, 08–10
  • Introduction to estimation theory.
  • Orthodox versus Bayesian approach.
  • Performance metrics: bias, variance, MSE, BMSE.
  • Cramer-Rao bound (CRB) and efficiency.
  • Kay-I Chapters 1-2, Sections 3.1-3.5
Lecture 6 September 11, 13–15
  • CRB for vector parameters.
  • Slepian-Bang's formula.
  • Nuisance parameters and decoupling.
  • Application example: source localization using time-of-arrival measurements.
  • CRB for the linear model with Gaussian noise.
  • Kay-I Sections 3.6-3.9, 3.11
Tutorial 2 September 16, 08–10
  • Walk-through by the instructor
    • 2025-10-29 (Problem 2)
  • Problems from Kay-II
    • 5.2, 5.3, 5.10, 5.14, 5.18
  • Problems from Kay-I
    • 1.1, 1.4, 1.5, 2.9
  • Problems from the "additional problems" document
    • Number 3
Tutorial 3 September 18, 13–15
  • Walk-through by the instructor
    • 2026-08-17 (Problem 5)
  • Problems from Kay-I
    • 3.1, 3.15, 3.19, 3.9, 4.11, 6.1, 6.3, 6.15, 6.16
  • Problems from the "additional problems" document
    • Number 4
Lecture 7 September 21, 13–15
  • MVU estimator for linear model with Gaussian noise.
  • BLUE estimator for linear model with arbitrary noise.
  • Application example: tapped-delay line identification.
  • Application example: two-tone model with sinusoids in noise.
  • Kay-I Chapters 4, 6
Lecture 8 September 25, 13–15
  • Maximum-likelihood estimation.
  • Asymptotic efficiency.
  • Parameter transformations.
  • Kay-I Sections 7.1-7.8, 7.10
Lecture 9 September 28, 13–15
  • Linear and non-linear least-squares.
  • Separable models.
  • Method of moments.
  • First-order approximations.
  • Kay-I Sections 8.1-8.4, 8.9, 9.1-9.5
Tutorial 4 September 30, 08–10
  • Walk-through by the instructor
    • 2026-08-17 (Problem 1)
  • Problems from Kay-I
    • 7.1, 7.3, 7.10, 7.20
  • Problems from the "additional problems" document
    • Number 5
  • Backup time
Lecture 10 October 02, 13–15
  • Bayesian estimation.
  • MMSE and LMMSE estimators.
  • Nuisance parameters in the Bayesian and orthodox paradigms.
  • Kay-I Sections 10.1-10.7, Chapter 11, Sections 12.1-12.3, 12.5
Lecture 11 October 05, 13–15
  • Detection of signals with unknown parameters.
  • Generalized likelihood ratio test (GLRT).
  • GLRT for linear models with Gaussian noise.
  • Performance of the GLRT for linear models with Gaussian noise.
  • Kay-II Sections 6.1-6.4, 7.1-7.6
Tutorial 5 October 05, 15–17
  • Walk-through by the instructor
    • TBD
  • Problems from Kay-I
    • 7.9, 8.1, 8.3, 8.5, 8.7, 9.1, 9.7
Tutorial 6 October 07, 08–10
  • Walk-through by the instructor
    • 2026-08-17 (Problem 3)
  • Problems from Kay-I
    • 10.6, 10.9, 10.11, 11.3, 11.9, 11.16, 12.2
  • Problems from the "additional problems" document
    • Number 6
Lecture 12 October 09, 13–15
  • GLRT for separable models and for problems with unknown noise statistics.
  • Summary and outlook
  • Kay-II Sections 7.7-7.8, 8.1-8.3, 9.1-9.4
Tutorial 7 October 12, 13–15
  • Walk-through by the instructor
    • 2026-01-09 (Problem 2)
  • Problems from Kay-II
    • 6.6, 6.10, 7.1, 7.2, 7.3, 7.9, 7.7, 7.10
Tutorial 8 October 19, 13–15
  • Walk-through by the instructor
    • 2025-10-29 (Problem 5)
  • Problems from Kay-II
    • 7.23, 7.25, 8.7, 8.8, 9.4, 9.11, 9.13
  • Problems from the "additional problems" document
    • Number 7

Lab Schedule

Group Time Lab Comment
Group A September 14, 13–15 Lab 1
Group B September 15, 17–19 Lab 1
Group A September 21, 15–17 Lab 2
Group B September 23, 08–10 Lab 2
Group A September 28, 15–17 Lab 1 Examination
Group B September 29, 17–19 Lab 1 Examination
Group A October 14, 08–10 Lab 2 Examination
Group B October 16, 13–15 Lab 2 Examination