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cs4780
0:57:28
CS4780 Transformers (additional lecture 2023)
0:47:25
Machine Learning Lecture 31 'Random Forests / Bagging' -Cornell CS4780 SP17
0:49:42
Lecture 3 'k-nearest neighbors' -Cornell CS4780 SP17
0:49:57
Lecture 5 'Perceptron' -Cornell CS4780 SP17
0:47:49
Lecture 1 'Supervised Learning Setup' -Cornell CS4780 Machine Learning for Decision Making SP17
0:52:41
Machine Learning Lecture 26 'Gaussian Processes' -Cornell CS4780 SP17
0:47:39
Machine Learning Lecture 11 'Logistic Regression' -Cornell CS4780 SP17
0:49:59
Machine Learning Lecture 14 '(Linear) Support Vector Machines' -Cornell CS4780 SP17
0:49:19
Machine Learning Lecture 12 'Gradient Descent / Newton's Method' -Cornell CS4780 SP17
0:47:43
Lecture 4 'Curse of Dimensionality / Perceptron' -Cornell CS4780 SP17
0:49:26
Lecture 6 'Perceptron Convergence Proof' -Cornell CS4780 SP17
0:49:39
Machine Learning Lecture 35 'Neural Networks / Deep Learning' -Cornell CS4780 SP17
0:48:59
Machine Learning Lecture 28 'Ball Trees / Decision Trees' -Cornell CS4780 SP17
0:46:33
Machine Learning Lecture 16 'Empirical Risk Minimization' -Cornell CS4780 SP17
0:48:50
Lecture 7 'Estimating Probabilities from Data: Maximum Likelihood Estimation' -Cornell CS4780 SP17
0:49:43
Machine Learning Lecture 30 'Bagging' -Cornell CS4780 SP17
0:51:57
Machine Learning Lecture 36 'Neural Networks / Deep Learning Continued' -Cornell CS4780 SP17
0:50:23
Machine Learning Lecture 29 'Decision Trees / Regression Trees' -Cornell CS4780 SP17
0:39:50
Machine Learning Lecture 13 'Linear / Ridge Regression' -Cornell CS4780 SP17
0:50:04
Machine Learning Lecture 15 '(Linear) Support Vector Machines continued' -Cornell CS4780 SP17
0:52:30
Machine Learning Lecture 17 'Regularization / Review' -Cornell CS4780 SP17
0:44:28
Machine Learning Lecture 8 'Estimating Probabilities from Data: Naive Bayes' -Cornell CS4780 SP17
0:48:27
Machine Learning Lecture 32 'Boosting' -Cornell CS4780 SP17
0:50:48
Machine Learning Lecture 18 'Review Lecture II' -Cornell CS4780 SP17
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