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Mathematics for Machine Learning
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Mathematics for Machine Learning
English
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Machine Learning Blink 1.1 (types of learners)
Resources
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Playlist (50 of 59 videos)
Playlist (50 of 59 videos)
1
Machine Learning Blink 1.1 (types of learners)
10:05
2
Machine Learning Blink 1.2 (training and testing sets)
28:25
3
Machine Learning Blink 1.3 (cross-validation)
23:37
4
Machine Learning Blink 2.1 (Recap and Outline)
4:55
5
Machine Learning Blink 2.2 (anchoring randomization in cross-validation)
6:50
6
Machine Learning Blink 2.3 (supervised learning: regression basic concept)
11:34
7
Machine Learning Blink 2.4 (supervised learning: classification basic concept)
11:55
8
Machine Learning Blink 2.5 (unsupervised learning: clustering)
12:44
9
Machine Learning Blink 2.6 (semi-supervised learning: basic concept)
8:43
10
Machine Learning Blink 2.7 (semi-supervised vs transductive learning)
17:29
11
Machine Learning Blink 2.8 (SL, US, SSL, and TL: summary)
9:27
12
Machine Learning Blink 3.1 (know your data: data covariance, domain shift and more)
25:15
13
Machine Learning Blink 3.2 (know your data covariance and fractures)
42:01
14
Machine Learning Blink 3.3 (covariance visualization and interpretation)
3:25
15
Machine Learning Blink 3.4 (probability level curves using Euclidean & Mahalanobis distances)
11:04
16
Machine Learning Blink 3.5 (geometric covariance for 2D data interpretation)
7:04
17
Machine Learning Blink 3.6 (Hands-on step-by-step linear Naive Bayes classifier example)
36:34
18
Machine Learning Blink 3.7 (Hands-on step-by-step quadratic Naive Bayes classifier example)
14:12
19
Machine Learning Blink 4.1 (recap of ML3: data distribution, covariance, and bayes classifier)
18:04
20
Machine Learning Blink 4.2 (taxonomy of supervised learning and loss function differentiation)
35:23
21
Machine Learning Blink 4.3 (finding the minima of multivariate loss functions)
11:26
22
Machine Learning Blink 4.4 (gradient descent)
24:58
23
Machine Learning Blink 5.1 (Newton's method for loss function optimization)
45:06
24
Machine Learning Blink 5.2 (zigzagging and vanishing gradient issues in optimization)
20:36
25
Machine Learning Blink 6.1 (overview and recap)
11:52
26
Machine Learning Blink 6.2 (training of linear regression model)
39:31
27
Machine Learning Blink 6.3 (testing and evaluation of a trained linear regression model)
15:33
28
Machine Learning Blink 6.4 (non-linear logistic regression model)
29:38
29
Machine Learning Blink 6.5 (feature transformation "trick" for nonlinear regression problems)
12:50
30
Machine Learning Blink 7.1 (recap of linear regression and practical example)
23:06
31
Machine Learning Blink 7.2 (soft and margin perceptrons)
38:52
32
Machine Learning Blink 8.1 (recap on logistic regression and perceptrons)
15:37
33
Machine Learning Blink 8.2 (what is support vector machines (SVM)?)
24:31
34
Machine Learning Blink 8.3 (optimizing support vector machines using Lagrangian optimization)
1:00:55
35
Machine Learning Blink 8.4 (support vector machine driven from logistic regression)
16:07
36
Machine Learning Blink 9.1 (recap on SVM constrained optimization)
11:24
37
Machine Learning Blink 9.2 (Hands-on step-by-step Support Vector Machine example)
44:09
38
Machine Learning Blink 9.3 (SVM kernel trick for nonlinear classification)
9:21
39
Machine Learning Blink 9.4 (multi-class classification using linear classifiers)
13:41
40
Machine Learning Blink 9.5 (classifier evaluation using accuracy, sensitivity, specificity)
5:47
41
Machine Learning Blink 10.1 (Why dimensionality reduction?)
29:45
42
Machine Learning Blink 10.2 (Feature selection: wrapper and filter methods)
20:19
43
Machine Learning 10.3 (Feature selection and classifier training protocol: DOs and DONTs)
26:04
44
Machine Learning Blink 10.4 (What is sparse feature selection (LASSO method)?)
15:40
45
Machine Learning Blink 11.1 (Introduction to Principle Component Analysis PCA)
15:57
46
Machine Learning Blink 11.2 (PCA: what metric to choose to find the best projection directions?)
11:19
47
Machine Learning Blink 11.3 (PCA in a nutshell)
32:43
48
Machine Learning Blink 11.4 (Hands-on step-by-step formalization of PCA)
30:29
49
Machine Learning Blink 11.5 (How to use PCA to train and test a supervised learner)
19:37
50
Machine Learning Blink 11.6 (Constrained PCA optimization and Lagrangian method)
13:40
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