Канал: Stanley Chan

Current and Future of NASA Telescopes

Current and Future of NASA Telescopes

Structured Kernel Estimation for Photon-Limited Deconvolution

Structured Kernel Estimation for Photon-Limited Deconvolution

Lecture 31 Law of Large Numbers

Lecture 31 Law of Large Numbers

Lecture 34 Examples of Law of Large Numbers and Central Limit Theorem

Lecture 34 Examples of Law of Large Numbers and Central Limit Theorem

Lecture 29 High Dimensional Gaussian

Lecture 29 High Dimensional Gaussian

Lecture 32 Markov and Chebyshev Inequalities

Lecture 32 Markov and Chebyshev Inequalities

Lecture 30 Moment Generating and Characteristic Functions

Lecture 30 Moment Generating and Characteristic Functions

Lecture 28 Random Vectors

Lecture 28 Random Vectors

Lecture 27 Sum of Two Random Variables

Lecture 27 Sum of Two Random Variables

Lecture 26 Conditional Expectation + Sum of Two Random Variables

Lecture 26 Conditional Expectation + Sum of Two Random Variables

Lecture 25 Conditional Expectation

Lecture 25 Conditional Expectation

Lecture 24 Conditional PDF

Lecture 24 Conditional PDF

Lecture 23 Joint Expectation and Covariance

Lecture 23 Joint Expectation and Covariance

Lecture 22 Joint PDF and CDF

Lecture 22 Joint PDF and CDF

Lecture 21 Generating Random Numbers

Lecture 21 Generating Random Numbers

Lecture 20 Function of Random Variables

Lecture 20 Function of Random Variables

Lecture 18 Uniform and Exponential Random Variables

Lecture 18 Uniform and Exponential Random Variables

Lecture 19 Gaussian Random Variables

Lecture 19 Gaussian Random Variables

Lecture 16 Probability Density Function

Lecture 16 Probability Density Function

Lecture 17 Cumulative Distribution Function (Continuous)

Lecture 17 Cumulative Distribution Function (Continuous)

Lecture 15 Poisson Random Variables

Lecture 15 Poisson Random Variables

Lecture 14 Binomial + Geometric Random Variables

Lecture 14 Binomial + Geometric Random Variables

Lecture 13 Bernoulli Random Variables

Lecture 13 Bernoulli Random Variables

Lecture 12 Moments

Lecture 12 Moments

Lecture 11 Expectation

Lecture 11 Expectation

Lecture 9 Random Variables

Lecture 9 Random Variables

Lecture 10 CDF (Discrete Case)

Lecture 10 CDF (Discrete Case)

Lecture 8 Total Probability

Lecture 8 Total Probability

Lecture 7 Bayes Theorem

Lecture 7 Bayes Theorem

Lecture 6 Conditional Probability

Lecture 6 Conditional Probability

Lecture 5 Probability Axioms

Lecture 5 Probability Axioms

Lecture 4 Probability Space

Lecture 4 Probability Space

Lecture 3: Set theory

Lecture 3: Set theory

Lecture 2: Integration, linear algebra, combinatorics

Lecture 2: Integration, linear algebra, combinatorics

Lecture 1 Review: Series. Approximation.

Lecture 1 Review: Series. Approximation.

Lecture 0 Introduction

Lecture 0 Introduction

CVPR 2022 Tutorial Part 1

CVPR 2022 Tutorial Part 1

CVPR 2022 Tutorial Part 3

CVPR 2022 Tutorial Part 3

CVPR 2022 Tutorial Part 0

CVPR 2022 Tutorial Part 0

CVPR 2022 Tutorial Part 2

CVPR 2022 Tutorial Part 2

ICASSP 2022 Photon Limited Deconvolution

ICASSP 2022 Photon Limited Deconvolution

Thesis Defense: Abhiram Gnanasambandam

Thesis Defense: Abhiram Gnanasambandam

ICCV 2021 Optical Adversarial Attack

ICCV 2021 Optical Adversarial Attack

Chapter 3 Exercise 12

Chapter 3 Exercise 12

Chapter 3 Exercise 11

Chapter 3 Exercise 11

Chapter 3 Exercise 10

Chapter 3 Exercise 10

Chapter 4 Exercise 5

Chapter 4 Exercise 5

Chapter 4 Exercise 3

Chapter 4 Exercise 3

Chapter 4 Exercise 2

Chapter 4 Exercise 2

Chapter 5 Exercise 11

Chapter 5 Exercise 11

Chapter 4 Exercise 4

Chapter 4 Exercise 4

Chapter 5 Exercise 7

Chapter 5 Exercise 7

Chapter 5 Exercise 18

Chapter 5 Exercise 18

Chapter 4 Exercise 1

Chapter 4 Exercise 1

Chapter 5 Exercise 17

Chapter 5 Exercise 17

Chapter 5 Exercise 5

Chapter 5 Exercise 5

Chapter 5 Exercise 6

Chapter 5 Exercise 6

Chapter 3 Exercise 13

Chapter 3 Exercise 13

Chapter 3 Exercise 14

Chapter 3 Exercise 14

Chapter 10 Exercise 5

Chapter 10 Exercise 5

Chapter 10 Exercise 11

Chapter 10 Exercise 11

Chapter 6 Exercise 19

Chapter 6 Exercise 19

Chapter 10 Exercise 10

Chapter 10 Exercise 10

Chapter 5 Exercise 13

Chapter 5 Exercise 13

Chapter 10 Exercise 6

Chapter 10 Exercise 6

Chapter 10 Exercise 4

Chapter 10 Exercise 4

Chapter 5 Exercise 12

Chapter 5 Exercise 12

Homework 9 Problem 3

Homework 9 Problem 3

Chapter 10 Exercise 13

Chapter 10 Exercise 13

Chapter 8 Exercise 1

Chapter 8 Exercise 1

Chapter 10 Exercise 2

Chapter 10 Exercise 2

Chapter 10 Exercise 12

Chapter 10 Exercise 12

Homework 12 Problem 8

Homework 12 Problem 8

Chapter 4 Exercise 6

Chapter 4 Exercise 6

Chapter 10 Exercise 7

Chapter 10 Exercise 7

Chapter 5 Exercise 15

Chapter 5 Exercise 15

Chapter 8 Exercise 2

Chapter 8 Exercise 2

Chapter 5 Exercise 1

Chapter 5 Exercise 1

Chapter 4 Exercise 7

Chapter 4 Exercise 7

Chapter 5 Exercise 4

Chapter 5 Exercise 4

Chapter 10 Exercise 3

Chapter 10 Exercise 3

Chapter 5 Exercise 14

Chapter 5 Exercise 14

CVPR 2021 Imaging through Turbulence

CVPR 2021 Imaging through Turbulence

CVPR 2021 Student-Teacher Learning from Clean Inputs to Noisy Inputs

CVPR 2021 Student-Teacher Learning from Clean Inputs to Noisy Inputs

ECE595ML Lecture 38-2 Conclusion: Practical Advices

ECE595ML Lecture 38-2 Conclusion: Practical Advices

ECE595ML Lecture 38-1 Conclusion: Practical Advices

ECE595ML Lecture 38-1 Conclusion: Practical Advices

ECE595ML Lecture 37-2 Robustness and Accuracy Trade-off

ECE595ML Lecture 37-2 Robustness and Accuracy Trade-off

ECE595ML Lecture 37-1 Robustness and Accuracy Trade-off

ECE595ML Lecture 37-1 Robustness and Accuracy Trade-off

ECE595ML Lecture 36-2 Defending Adversarial Attack

ECE595ML Lecture 36-2 Defending Adversarial Attack

ECE595ML Lecture 36-1 Defending Adversarial Attack

ECE595ML Lecture 36-1 Defending Adversarial Attack

ECE595ML Lecture 35-2 Maximum Loss Attack

ECE595ML Lecture 35-2 Maximum Loss Attack

ECE595ML Lecture 35-1 Maximum Loss Attack

ECE595ML Lecture 35-1 Maximum Loss Attack

ECE595ML Lecture 34-2 Minimum Distance Attack

ECE595ML Lecture 34-2 Minimum Distance Attack

ECE595ML Lecture 34-1 Minimum Distance Attack

ECE595ML Lecture 34-1 Minimum Distance Attack

ECE595ML Lecture 33-2 Overview of Adversarial Attack

ECE595ML Lecture 33-2 Overview of Adversarial Attack

ECE595ML Lecture 33-1 Overview of Adversarial Attack

ECE595ML Lecture 33-1 Overview of Adversarial Attack

ECE595ML Lecture 32-3 Validation

ECE595ML Lecture 32-3 Validation

ECE595ML Lecture 32-2 Validation

ECE595ML Lecture 32-2 Validation

ECE595ML Lecture 32-1 Validation

ECE595ML Lecture 32-1 Validation

ECE595ML Lecture 31-3 Regularization

ECE595ML Lecture 31-3 Regularization

ECE595ML Lecture 31-2 Regularization

ECE595ML Lecture 31-2 Regularization

ECE595ML Lecture 31-1 Regularization

ECE595ML Lecture 31-1 Regularization

ECE595ML Lecture 30-2 Overfitting

ECE595ML Lecture 30-2 Overfitting

ECE595ML Lecture 30-1 Overfitting

ECE595ML Lecture 30-1 Overfitting

ECE595ML Lecture 29-2 Bias and Variance

ECE595ML Lecture 29-2 Bias and Variance

ECE595ML Lecture 29-1 Bias and Variance

ECE595ML Lecture 29-1 Bias and Variance

ECE595ML Lecture 28-2 Sample and Model Complexity

ECE595ML Lecture 28-2 Sample and Model Complexity

ECE595ML Lecture 28-1 Sample and Model Complexity

ECE595ML Lecture 28-1 Sample and Model Complexity

ECE595ML Lecture 27-2 VC Dimension

ECE595ML Lecture 27-2 VC Dimension

ECE595ML Lecture 27-1 VC Dimension

ECE595ML Lecture 27-1 VC Dimension

ECE595ML Lecture 26-2 Growth Function

ECE595ML Lecture 26-2 Growth Function

ECE595ML Lecture 26-1 Growth Function

ECE595ML Lecture 26-1 Growth Function

ECE595ML Lecture 25-2 Generalization Bound

ECE595ML Lecture 25-2 Generalization Bound

ECE595ML Lecture 25-1 Generalization Bound

ECE595ML Lecture 25-1 Generalization Bound

ECE595ML Lecture 24-2 Probably Approximately Correct

ECE595ML Lecture 24-2 Probably Approximately Correct

ECE595ML Lecture 24-1 Probably Approximately Correct

ECE595ML Lecture 24-1 Probably Approximately Correct

ECE595ML Lecture 23-3 Probability Inequality

ECE595ML Lecture 23-3 Probability Inequality

ECE595ML Lecture 23-2 Probability Inequality

ECE595ML Lecture 23-2 Probability Inequality

ECE595ML Lecture 23-1 Probability Inequality

ECE595ML Lecture 23-1 Probability Inequality

ECE595ML Lecture 22-3 Is Learning Feasible?

ECE595ML Lecture 22-3 Is Learning Feasible?

ECE595ML Lecture 22-2 Is Learning Feasible?

ECE595ML Lecture 22-2 Is Learning Feasible?

ECE595ML Lecture 22-1 Is Learning Feasible?

ECE595ML Lecture 22-1 Is Learning Feasible?

ECE595ML Lecture 21-2 Soft SVM and Kernel SVM

ECE595ML Lecture 21-2 Soft SVM and Kernel SVM

ECE595ML Lecture 21-1 Soft SVM and Kernel SVM

ECE595ML Lecture 21-1 Soft SVM and Kernel SVM

ECE595ML Lecture 20-2 Dual SVM

ECE595ML Lecture 20-2 Dual SVM

ECE595ML Lecture 20-1 Dual SVM

ECE595ML Lecture 20-1 Dual SVM

ECE595ML Lecture 19-2 Intro to SVM

ECE595ML Lecture 19-2 Intro to SVM

ECE595ML Lecture 19-1 Intro to SVM

ECE595ML Lecture 19-1 Intro to SVM

ECE595ML Lecture 18-2 Multi-layer Perceptron and Back Propagation

ECE595ML Lecture 18-2 Multi-layer Perceptron and Back Propagation

ECE595ML Lecture 18-1 Multi-layer Perceptron and Back Propagation

ECE595ML Lecture 18-1 Multi-layer Perceptron and Back Propagation

ECE595ML Lecture 17-3 Perceptron Algorithm

ECE595ML Lecture 17-3 Perceptron Algorithm

ECE595ML Lecture 17-2 Perceptron Algorithm

ECE595ML Lecture 17-2 Perceptron Algorithm

ECE595ML Lecture 17-1 Perceptron Algorithm

ECE595ML Lecture 17-1 Perceptron Algorithm

ECE595ML Lecture 16-2 Perceptron

ECE595ML Lecture 16-2 Perceptron

ECE595ML Lecture 16-1 Perceptron

ECE595ML Lecture 16-1 Perceptron

ECE595ML Lecture 15-3 Logistic Regression Algorithm

ECE595ML Lecture 15-3 Logistic Regression Algorithm

ECE595ML Lecture 15-2 Logistic Regression Algorithm

ECE595ML Lecture 15-2 Logistic Regression Algorithm

ECE595ML Lecture 15-1 Logistic Regression Algorithm

ECE595ML Lecture 15-1 Logistic Regression Algorithm

ECE595ML Lecture 14-3 Logistic Loss and Convexity

ECE595ML Lecture 14-3 Logistic Loss and Convexity

ECE595ML Lecture 14-2 Logistic Loss and Convexity

ECE595ML Lecture 14-2 Logistic Loss and Convexity

ECE595ML Lecture 14-1 Logistic Loss and Convexity

ECE595ML Lecture 14-1 Logistic Loss and Convexity

ECE595ML Lecture 13-2 Connecting Bayesian and Regression

ECE595ML Lecture 13-2 Connecting Bayesian and Regression

ECE595ML Lecture 13-1 Connecting Bayesian and Regression

ECE595ML Lecture 13-1 Connecting Bayesian and Regression

ECE595ML Lecture 12-2 Bayesian Priors

ECE595ML Lecture 12-2 Bayesian Priors

ECE595ML Lecture 11-2 Parameter Estimation

ECE595ML Lecture 11-2 Parameter Estimation

ECE595ML Lecture 08-1 Hand-crafted and deep features

ECE595ML Lecture 08-1 Hand-crafted and deep features

ECE595ML Lecture 08-2 Hand-crafted and deep features

ECE595ML Lecture 08-2 Hand-crafted and deep features

ECE595ML Lecture 03-2 Nonlinearity and Kernel Trick

ECE595ML Lecture 03-2 Nonlinearity and Kernel Trick

ECE595ML Lecture 01-1 Linear Regression

ECE595ML Lecture 01-1 Linear Regression

ECE595ML Lecture 05-1 Gradient Descent and Stochastic Gradient Descent

ECE595ML Lecture 05-1 Gradient Descent and Stochastic Gradient Descent

ECE595ML Lecture 09-3 Bayesian Decision Rule

ECE595ML Lecture 09-3 Bayesian Decision Rule

ECE595ML Lecture 02-2 Ridge and LASSO regression

ECE595ML Lecture 02-2 Ridge and LASSO regression

ECE595ML Lecture 09-2 Bayesian Decision Rule

ECE595ML Lecture 09-2 Bayesian Decision Rule

ECE595ML Lecture 07-2 Principal Component Analysis

ECE595ML Lecture 07-2 Principal Component Analysis

ECE595ML Lecture 02-1 Ridge and LASSO regression

ECE595ML Lecture 02-1 Ridge and LASSO regression

ECE595ML Lecture 04-2 Optimality and Convexity

ECE595ML Lecture 04-2 Optimality and Convexity

ECE595ML Lecture 01-2 Linear Regression

ECE595ML Lecture 01-2 Linear Regression

ECE595ML Lecture 06-2 Linear Separability

ECE595ML Lecture 06-2 Linear Separability

ECE595ML Lecture 07-1 Principal Component Analysis

ECE595ML Lecture 07-1 Principal Component Analysis

ECE595ML Lecture 03-1 Nonlinearity and Kernel Trick

ECE595ML Lecture 03-1 Nonlinearity and Kernel Trick

ECE595ML Lecture 04-1 Optimality and Convexity

ECE595ML Lecture 04-1 Optimality and Convexity

ECE595ML Lecture 12-1 Bayesian Priors

ECE595ML Lecture 12-1 Bayesian Priors

ECE595ML Lecture 05-2 Gradient Descent and Stochastic Gradient Descent

ECE595ML Lecture 05-2 Gradient Descent and Stochastic Gradient Descent

ECE595ML Lecture 06-3 Linear Separability

ECE595ML Lecture 06-3 Linear Separability

ECE595ML Lecture 04-3 Optimality and Convexity

ECE595ML Lecture 04-3 Optimality and Convexity

ECE595ML Lecture 10 Minimum Probability of Error

ECE595ML Lecture 10 Minimum Probability of Error

ECE595ML Lecture 09-1 Bayesian Decision Rule

ECE595ML Lecture 09-1 Bayesian Decision Rule

ECE595ML Lecture 11-1 Parameter Estimation

ECE595ML Lecture 11-1 Parameter Estimation

ECE595ML Lecture 08-3 Hand-crafted and deep features

ECE595ML Lecture 08-3 Hand-crafted and deep features

ECE595ML Lecture 06-1 Linear Separability

ECE595ML Lecture 06-1 Linear Separability

ECE 302 Lecture A.9 Cross correlation through LTI systems

ECE 302 Lecture A.9 Cross correlation through LTI systems

ECE 302 Lecture A.7 Linear time invariant systems

ECE 302 Lecture A.7 Linear time invariant systems

ECE 302 Lecture A.8 Mean and autocorrelation through LTI systems

ECE 302 Lecture A.8 Mean and autocorrelation through LTI systems

ECE 302 Lecture A.5 Wide sense stationary

ECE 302 Lecture A.5 Wide sense stationary

ECE 302 Lecture A.6 Power spectral density

ECE 302 Lecture A.6 Power spectral density

ECE 302 Lecture A.3 Autocorrelation Function

ECE 302 Lecture A.3 Autocorrelation Function

ECE 302 Lecture A.1 Random Processes

ECE 302 Lecture A.1 Random Processes

ECE 302 Lecture A.2 Mean Functions

ECE 302 Lecture A.2 Mean Functions

ECE 302 Lecture A.4 Autocovariance, and Independent Processes

ECE 302 Lecture A.4 Autocovariance, and Independent Processes

ECE 302 Lecture 6.2 Characteristic functions

ECE 302 Lecture 6.2 Characteristic functions

ECE 302 Lecture 6.1 Moment generating functions

ECE 302 Lecture 6.1 Moment generating functions

ECE 302 Lecture 5.7 Sum of two random variables (examples)

ECE 302 Lecture 5.7 Sum of two random variables (examples)

ECE 302 Lecture 5.11 Principal Component Analysis

ECE 302 Lecture 5.11 Principal Component Analysis

ECE 302 Lecture 5.9 Multi-dimensional Gaussian

ECE 302 Lecture 5.9 Multi-dimensional Gaussian

ECE 302 Lecture 5.10 Gaussian whitening

ECE 302 Lecture 5.10 Gaussian whitening

ECE 302 Lecture 5.8 Random vectors

ECE 302 Lecture 5.8 Random vectors

ECE 302 Lecture 5.5 Conditional expectation

ECE 302 Lecture 5.5 Conditional expectation

ECE 302 Lecture 5.6 Sum of two random variables

ECE 302 Lecture 5.6 Sum of two random variables

ECE 302 Lecture 5.2 Joint Expectations

ECE 302 Lecture 5.2 Joint Expectations

ECE 302 Lecture 5.3 Correlation and Covariance

ECE 302 Lecture 5.3 Correlation and Covariance

ECE 302 Lecture 5.1 Joint PDF, PMF, CDF

ECE 302 Lecture 5.1 Joint PDF, PMF, CDF

ECE 302 Lecture 5.4 Conditional PMF and PDF

ECE 302 Lecture 5.4 Conditional PMF and PDF

ECE 302 Lecture 4.9 Generating random numbers

ECE 302 Lecture 4.9 Generating random numbers

ECE 302 Lecture 4.7 Gaussian random variables

ECE 302 Lecture 4.7 Gaussian random variables

ECE 302 Lecture 4.8 Transformation of random variables

ECE 302 Lecture 4.8 Transformation of random variables

ECE 302 Lecture 4.5 Uniform random variables

ECE 302 Lecture 4.5 Uniform random variables

ECE 302 Lecture 4.1 Probability density functions

ECE 302 Lecture 4.1 Probability density functions

ECE 302 Lecture 4.2 Expectation (Continuous)

ECE 302 Lecture 4.2 Expectation (Continuous)

ECE 302 Lecture 4.3 Cumulative distribution functions (continuous)

ECE 302 Lecture 4.3 Cumulative distribution functions (continuous)

ECE 302 Lecture 4.6 Exponential random variables

ECE 302 Lecture 4.6 Exponential random variables