Clustering (4): Gaussian Mixture Models and EM
Alexander Ihler
Clustering (4): Gaussian Mixture Models and EM
17:11
Clustering (3): K-Means Clustering
Alexander Ihler
Clustering (3): K-Means Clustering
15:02
Clustering (2): Hierarchical Agglomerative Clustering
Alexander Ihler
Clustering (2): Hierarchical Agglomerative Clustering
12:34
Clustering (1): Basics
Alexander Ihler
Clustering (1): Basics
6:00
Neural Networks (2): Backpropagation
Alexander Ihler
Neural Networks (2): Backpropagation
14:49
Neural Networks (1): Basics
Alexander Ihler
Neural Networks (1): Basics
13:52
Support Vector Machines (3): Kernels
Alexander Ihler
Support Vector Machines (3): Kernels
15:09
Support Vector Machines (2): Dual & soft-margin forms
Alexander Ihler
Support Vector Machines (2): Dual & soft-margin forms
14:09
Support Vector Machines (1): Linear SVMs, primal form
Alexander Ihler
Support Vector Machines (1): Linear SVMs, primal form
10:55
VC Dimension
Alexander Ihler
VC Dimension
17:42
Linear classifiers (2): Learning parameters
Alexander Ihler
Linear classifiers (2): Learning parameters
21:06
Linear classifiers (1): Basics
Alexander Ihler
Linear classifiers (1): Basics
14:15
Linear regression (6): Regularization
Alexander Ihler
Linear regression (6): Regularization
8:30
Linear regression (2): Gradient descent
Alexander Ihler
Linear regression (2): Gradient descent
14:21
Linear regression (4): Nonlinear features
Alexander Ihler
Linear regression (4): Nonlinear features
6:48
Linear regression (5): Bias and variance
Alexander Ihler
Linear regression (5): Bias and variance
4:49
Linear regression (3): Normal equations
Alexander Ihler
Linear regression (3): Normal equations
8:22
Linear regression (1): Basics
Alexander Ihler
Linear regression (1): Basics
5:47
Introduction (2): Data and Visualization
Alexander Ihler
Introduction (2): Data and Visualization
8:49
Introduction (1): AI & Machine Learning
Alexander Ihler
Introduction (1): AI & Machine Learning
6:50
Introduction (3): Supervised Learning
Alexander Ihler
Introduction (3): Supervised Learning
9:52
Introduction (4): Complexity and Overfitting
Alexander Ihler
Introduction (4): Complexity and Overfitting
6:22
Bayes Classifiers (2): Naive Bayes
Alexander Ihler
Bayes Classifiers (2): Naive Bayes
15:04
Bayes Classifiers (1)
Alexander Ihler
Bayes Classifiers (1)
11:51
Nearest Neighbor (1)
Alexander Ihler
Nearest Neighbor (1)
7:46
Nearest neighbor (2): k-nearest neighbor
Alexander Ihler
Nearest neighbor (2): k-nearest neighbor
9:54
Multivariate Gaussian distributions
Alexander Ihler
Multivariate Gaussian distributions
14:49
PCA, SVD
Alexander Ihler
PCA, SVD
17:37
Clustering
Alexander Ihler
Clustering
32:09
Review: Probability
Alexander Ihler
Review: Probability
25:04
Ensembles (4): AdaBoost
Alexander Ihler
Ensembles (4): AdaBoost
19:36
Ensembles (3): Gradient Boosting
Alexander Ihler
Ensembles (3): Gradient Boosting
11:48
Ensembles (2): Bagging
Alexander Ihler
Ensembles (2): Bagging
16:08
Ensembles (1): Basics
Alexander Ihler
Ensembles (1): Basics
6:53
Decision Trees (2)
Alexander Ihler
Decision Trees (2)
25:27
Decision Trees (1)
Alexander Ihler
Decision Trees (1)
5:33