Authors

N

• Nag, Aditya
  Modules: 3, Collections: 0
• Nagisetty, Sivakiran
  Modules: 7, Collections: 0
• Nakagawa, Misato
  Modules: 1, Collections: 0
• Nandi, Arnab
  Modules: 1, Collections: 0
• Naranapatty, Krishnan
  Modules: 1, Collections: 0
• Narayan, Neil
  Modules: 7, Collections: 0
• NASA
  Modules: 1, Collections: 0
• NCPEA, National Council of Professors
  Modules: 26, Collections: 3
• Neddermeyer, Robert
  Modules: 3, Collections: 1
• Nelson, Judith
  Modules: 1, Collections: 0
• Nera, Alessia
  Modules: 1, Collections: 0
• Newell, David
  Modules: 3, Collections: 1
• Ng, Elliot
  Modules: 13, Collections: 0
• Nguyen, Thu
  Modules: 2, Collections: 0
• Nguyen, Dung
  Modules: 26, Collections: 1
• Nguyen, Long
  Modules: 0, Collections: 1
• Nguyen, Phuong
  Modules: 5, Collections: 2
• Nguyen, Quoclinh
  Modules: 1, Collections: 0
• Nguyen, Quoclinh
  Modules: 3, Collections: 0
• Nguyen, Thang
  Modules: 9, Collections: 1
• Nguyen-Le, Sinh
  Modules: 10, Collections: 0
• Nichter, Mary
  Modules: 1, Collections: 0
• Nie, Youfeng
  Modules: 1, Collections: 0
• Nordin, Justin
  Modules: 1, Collections: 0
• novice.com, bourse-
  Modules: 12, Collections: 1
• Novich, Scott
  Modules: 2, Collections: 0
• Nowak, Marcin
  Modules: 1, Collections: 1
• Nowak, Robert
  Modules: 52, Collections: 2

Robert Nowak

Type	Title
	Intro to Digital Signal Processing
	Statistical Learning Theory
	2D DFT
	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