17:11
Clustering (4): Gaussian Mixture Models and EM
15:02
Clustering (3): K-Means Clustering
12:34
Clustering (2): Hierarchical Agglomerative Clustering
06:00
Clustering (1): Basics
14:49
Neural Networks (2): Backpropagation
13:52
Neural Networks (1): Basics
15:09
Support Vector Machines (3): Kernels
14:09
Support Vector Machines (2): Dual & soft-margin forms
10:55
Support Vector Machines (1): Linear SVMs, primal form
17:42
VC Dimension
21:06
Linear classifiers (2): Learning parameters
14:15
Linear classifiers (1): Basics
08:30
Linear regression (6): Regularization
14:21
Linear regression (2): Gradient descent
06:48
Linear regression (4): Nonlinear features
04:49
Linear regression (5): Bias and variance
08:22
Linear regression (3): Normal equations
05:47
Linear regression (1): Basics
08:49
Introduction (2): Data and Visualization
06:50
Introduction (1): AI & Machine Learning
09:52
Introduction (3): Supervised Learning
06:22
Introduction (4): Complexity and Overfitting
15:04
Bayes Classifiers (2): Naive Bayes
11:51
Bayes Classifiers (1)
07:46
Nearest Neighbor (1)
09:54
Nearest neighbor (2): k-nearest neighbor
14:49
Multivariate Gaussian distributions
17:37
PCA, SVD
32:09
Clustering
25:04
Review: Probability
19:36
Ensembles (4): AdaBoost
11:48
Ensembles (3): Gradient Boosting
16:08
Ensembles (2): Bagging
06:53
Ensembles (1): Basics
25:27
Decision Trees (2)
05:33
Decision Trees (1)
X
About
Tags