ELE530 Theory of Detection and Estimation
Lectures
February 2nd: Introduction to Detection and Estimation
Slides: Introduction.
Readings: Chapter I in H.V. Poor's book.
February 4th: Minimum Risk and Minimax Hypothesis Testing
Slides: Minimum Risk and Minimax.
Readings: Sections II.A--II.C in H.V. Poor's book.
February 9th: Neyman-Pearson Hypothesis Testing
Slides: Minimax and Neyman-Pearson.
Homework: Assignment 1. Solution.
Readings: Sections II.C--II.E in H.V. Poor's book. Chapter 10 in L. Wasserman's book.
February 16th: Neyman-Pearson Hypothesis Testing
Slides: Neyman-Pearson.
Readings: Sections II.D--II.E in H.V. Poor's book. Chapter 10 in L. Wasserman's book.
February 18th: Signal Detection
Slides: Signal Detection.
Movies: Optimal Signaling and Whitening.
Readings: Sections III.A--III.B in H.V. Poor's book.
February 20th: Sequential Detection
Slides: Sequential Detection.
Sequential demo: demo.m.
Readings: Sections III.D in H.V. Poor's book.
February 23rd: Robust and Nonparametric Detection
Slides: Robust and Nonparametric Detection.
Homework: Assignment 2. Solution.
Readings: Sections III.e in H.V. Poor's book.
February 25th: Classification
Slides: Classification.
Readings: Chapter I in C. M. Bishop ``Pattern Recognition and Machine Learning''
March 2nd: Learning Theory
Slides: Learning Theory.
Readings: Chapter V in B. Schoelkopf and A. J. Smola ``Learning With Kernels''
March 4nd: Support Vector Machines
Slides: Support Vector Machines.
Homework: Assignment 3. Solution.
Readings: Chapter VII in B. Schoelkopf and A. J. Smola ``Learning With Kernels''
March 9th: Review Session
March 11th: Midterm Exam
Midterm Exam: Solution.
March 25th: Introduction to Estimation
Slides: Estimation.
Readings: Sections IV.A--IV.B in H.V. Poor's book. Section 2.4 in H Van Trees "Detection, Estimation and Modulation Theory: Part I".
March 30th: Maximum Likelihood Estimation
Slides: MLE.
Readings: Sections IV.C--IV.D in H.V. Poor's book. Section 2.4 in H Van Trees "Detection, Estimation and Modulation Theory: Part I".
April 1st: Maximum Likelihood Estimation
Slides: MLE.
Readings: Sections IV.C--IV.D in H.V. Poor's book. Section 2.4 in H Van Trees "Detection, Estimation and Modulation Theory: Part I".
April 6th: Further Aspects in Maximum Likelihood Estimation
Slides: More Estimation.
Homework: Assignment 4. Solution.
Readings: Sections IV-B, IV.E in H.V. Poor's book. Section 2.4 in H Van Trees "Detection, Estimation and Modulation Theory: Part I".
April 8th: Kalman Filtering
Slides: Kalman Filter.
Readings: Sections V.A-B in H.V. Poor's book.
April 13th: Kalman Filtering
Slides: Kalman Filter.
Readings: Sections V.A-B in H.V. Poor's book.
April 15th: Linear Estimation
Slides: Linear Estimation.
Homework: Assignment 5. Solution.
Readings: Sections V.C-D.1 in H.V. Poor's book.
April 20th: Regression
Slides: Regression.
Readings: Chapter 9 in B. Schoelkopf and A. J. Smola ``Learning With Kernels''
April 22nd: Gaussian Processes for Regression
Readings: Chapter 2 in C. Rasmussen and C. Williams ``Gaussian Processes for Machine Learning''
April 27th: Gaussian Processes for Classification
Readings: Chapter 3 in C. Rasmussen and C. Williams ``Gaussian Processes for Machine Learning''
April 29th: Introduction to Graphical Models
Slides: Graphical Models.
Readings: Chapter 1 and 2 in M. Wainwright1 and M. Jordan ``Graphical Models, Exponential Families, and Variational Inference'' and Chapter 26 in D. Mackay ``Information Theory, Inference, and Learning Algorithms''
Final Project. Due Date April 27th
Slides: Final Project.
Optional: Forward Selection.
Data: Data.
Kernel: Kernel function in Matlab.