Modern Theory of Detection and Estimation

Universidad Carlos III de Madrid

Course Description

  • Course Name

    Modern Theory of Detection and Estimation

  • Host University

    Universidad Carlos III de Madrid

  • Location

    Madrid, Spain

  • Area of Study

    Engineering Science and Math, Systems Engineering

  • Language Level

    Taught In English

  • Prerequisites

    Statistics, Calculus II, Systems & Circuits

  • Course Level Recommendations


    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • ECTS Credits

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    Modern Theory of Detection and Estimation
    Course Number: 214 - 14994
    ECTS credits: 6
    YEAR 3/ UPPER Division

    Statistics, Calculus II, Systems & Circuits


    After this course students will understand the principles of estimation and decision problems, and will become familiarized with the fundamental differences between the analytic and machine approaches that can be followed to solve them.  Students will understand that, for the correct understanding of these problems, it is necessary to master three basic probability theory elements: 1) the likelihood, 2) the difference between a priori and a posteriori uncertainty, and 3) Bayes' Theorem.  They will also understand the concepts of generalization and sufficient stastistics, as well as the bias vs variance tradeoff.  Finally, it will become apparent the advantages (both analytical and computational) inherent to Gaussian problems and linear solutions. (PO a)

    From a practical point of view, students will learn to identify the convenience of following an analytical or machine approach for concrete situations.  They will acquire the necessary knowledge to face an analytical resolution of a decision or estimation problem when complete statistical information is available, knowing also some semianalytical approaches for scenarios with partial information.  When no statistical information is available, they will know how to design a regression or classification model, using data sets for learning its parameters: splitting the available data into training, validation and test sets, and applying algorithms for model order selection and parameter adjustment.  Furthermore, different criteria for measuring the quality of deciders and estimators, as well as their generalization capabilities, will be introduced.  Finally, students will study how these tools for estimation and detection can be adapted to deal with temporal series, and to implement adaptive solutions. (PO b)

    During the course, students will study the previous concepts from a theoretical point of view, and will also apply them for the resolution of several study cases in practical sessions. During these sessions, students' work will help them improve the following general skills:
    * Ability to identify and understand particular estimation and decision problems, and to propose practical solutions taking into account the characteristics of such problems (availability of historic data, possible computational constraints, etc) (PO e)
    * Ability to design the experiments for the evaluation of the implemented estimators and deciders. (PO b)
    * Knowledge of a simulation and mathematical modeling software application, which is widely used in engineering (Matlab) (PO k)


    Block 0 - Introduction to Statistical and Machine Learning
       0.1. Estimation and classification concepts
       0.2. Examples of application of estimators and classifiers
       0.3. Analytical, semianalytical and machine methods
       0.4. Previous knowledge

    Block 1 - Analytical and Machine Estimation
       1.1. General view of the estimation problem: Analytical and Machine approaches
       1.2. Design of estimators under an analytical approach
          *ML estimation of deterministic parameters
          * Bayesian Estimation Theory.  Cost functions.  MSE, ML and MAP estimation.  Gaussian case.
          * Minimum Mean Square Error linear estimator
          * Bias and Variance of estimators
       1.3. Design of estimators under a machine approach
          * Design of machine estimators: general approach
          * Least Squares Linear Regression
          * Semilinear regression

    Block 2 - Analytical and Machine Decision
       2.1. General view of the decision problem: Analytical and Machine approaches
       2.2. Design of classifiers under an analytical approach
          * ML and MAP decision
          * Minimization of the expected cost: Optimal Bayesian decider
          * Binary classification.  LRT tests.  False Alarm, Miss, and Detection Probabilities.  Characteristic Curves (OC). Gaussian likelihoods.
       2.3. Design of classifiers under a machine approach
          * Train, validation and test data sets.  Generalization
          * Linear machine classifiers
          * Non-linear machine classifiers: semilinear models

    Block 3 - Temporal Series Filtering
       3.1. Transversal scheme for linear filtering.  Frequent configuration setups
       3.2. Mean Square Error minimization: Wiener-Hopf equation, the Wiener Filter, Canonical shape of the error surface
       3.3. Adaptive filtering.  Steepest Descent algorithm.  Stochastic approximations: the LMS filter


    Theory sessions consist of lectures in which the basic concepts of the course will be introduced, illustrating them with a large number of examples (POs a and e)

    Exercises and problems similar to those to be proposed in the exam will be solved.  Students will have problem statements available at the beginning of the course, so that they can work on them before they are solved in class. (POs a and e)

    Sessions in which students will apply the concepts presented in the course with the help of a computer.  Students will deal with estimation and classification problems with real data, and will have to evaluate the performance of the implemented systems (PO b). During these practical sessions students will use Matlab as the simulation tool. (PO k)


    The final mark of the course will be obtained by following the principles of continuous assessment:

    - Exercises and theory questions to be solved by the students by taking an intermediate exam (30% of the course mark);
    - During some of the practical sessions, students we be presented short estimation and decision/classification problems to be solved with the help of Matlab: 20% of the course mark;
    - Final exam consisting of a theory part with questions and short exercises, followed by several problems: 50% of the course mark.

    Students who do not follow the continuous evaluation procedure, will be assessed according to the general rules established by the University.

        C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Oxford (United Kingdom). 1995
        C.M. Bishop. Pattern Recognition and Machine Learning. New York, NY: Springer. 2006
        H. L. Van Trees. Detection, Estimation, and Modulation Theory (vol. I). New York, NY: Wiley. 1968
        R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification. New York, NY: Wiley. 2001

Course Disclaimer

Courses and course hours of instruction are subject to change.

ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.


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