ECE595ML Lecture 01-1 Linear Regression

(17:59)

ECE595ML Lecture 30-1 Overfitting

(20:31)

ECE595ML Lecture 16-1 Perceptron

(27:24)

ECE595ML Lecture 12-1 Bayesian Priors

(34:39)

ECE595ML Lecture 08-1 Hand-crafted and deep features

(19:5)

ECE595ML Lecture 03-1 Nonlinearity and Kernel Trick

(34:45)

ECE595ML Lecture 29-1 Bias and Variance

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ECE595ML Lecture 07-1 Principal Component Analysis

(33:32)

ECE595ML Lecture 19-1 Intro to SVM

(21:5)

ECE595ML Lecture 38-1 Conclusion: Practical Advices

(19:28)

ECE595ML Lecture 31-1 Regularization

(14:23)

ECE595ML Lecture 23-1 Probability Inequality

(31:14)

ECE595ML Lecture 35-1 Maximum Loss Attack

(31:14)

ECE595ML Lecture 24-2 Probably Approximately Correct

(19:31)

ECE595ML Lecture 28-1 Sample and Model Complexity

(18:20)

ECE595ML Lecture 22-1 Is Learning Feasible?

(21:38)

ECE595ML Lecture 13-1 Connecting Bayesian and Regression

(29:7)

ECE595ML Lecture 24-1 Probably Approximately Correct

(25:49)

ECE595ML Lecture 20-1 Dual SVM

(23:50)