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Robert Nowak

Type Title
Intro to Digital Signal Processing
Statistical Learning Theory
An Example of the Use of Sieves for Complexity Regularization in Denoising
Applications of VC Bound
Basic Elements of Statistical Decision Theory and Statistical Learning Theory
Chernoff's Bound and Hoeffding's Inequality
Classification Error Bounds
Complexity Regularization
Complexity Regularization for Squared Error Loss
Decision Trees
Denoising II: Adapting to Unknown Smoothness
DFT as a Matrix Operation
Digital Image Processing Basics
Discrete-Time Processing of CT Signals
Elements of Statistical Learning Theory
Error Bounds in Countably Infinite Spaces
Existence of the Minimum Variance Unbiased Estimator (MVUB)
Fast Convolution Using the FFT
Filtering with the DFT
Fundamental Probability Density Functions and Properties
Gauss-Markov Theorem and Wiener Filtering
Hypothesis Testing
Ideal Reconstruction of Sampled Signals
Image Restoration Basics
Images: 2D signals
Introduction to Adaptive Filtering
Introduction to Classification and Regression
Introduction to Complexity Regularization
Kalman Filtering Application
Kalman Filters
Linear Models
LMS Algorithm Analysis
Lower Performance Bounds for Estimators
Maximum Likelihood and Complexity Regularization
Maximum Likelihood Estimation
Maximum Likelihood Estimation
Minimum Probability of Error Decision Rule
Nonlinear Approximation and Wavelet Analysis
Plug-In Classifier and Histogram Classifier
Probably Approximately Correct (PAC) Learning
Sampling CT Signals: A Frequency Domain Perspective
Sufficient Statistics
The Bayes Risk Criterion in Hypothesis Testing
The Bayesian Paradigm
The Cramer-Rao Lower Bound
The DFT: Frequency Domain with a Computer Analysis
The FFT Algorithm
The Fisher-Neyman Factorization Theorem
The Minimum Variance Unbiased Estimator
The Q-function
The Vapnik-Chervonenkis Inequality
Vapnik-Chervonenkis Theory
Wiener Filtering and the DFT
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Total Collections: 1463
Total Modules: 24121