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AA 19/20 Lecture 20
AA 19/20 Lecture 21
AA 19/20 Lecture 19
AA 19/20 Lecture 22
AA 19/20 Lecture 7
Chapter 19   Part 1
AA 19/20 Lecture 9
AA 19/20 Lecture 4
EMT Chapter 20
AA 19/20 Lecture 6
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AA 19/20 Lecture 20

AA 19/20 Lecture 20

Fuzzy sets and clustering. Fuzzy c-means. Manifold learning. Second assignment.

AA 19/20 Lecture 21

AA 19/20 Lecture 21

Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Graphical methods, Hidden markov models.

AA 19/20 Lecture 19

AA 19/20 Lecture 19

Hierarchical Clustering. Agglomerative and Divisive Clustering.

AA 19/20 Lecture 22

AA 19/20 Lecture 22

Read more details and related context about AA 19/20 Lecture 22.

AA 19/20 Lecture 7

AA 19/20 Lecture 7

Generative models: naive bayes, bayes. Comparing classifiers.

Chapter 19   Part 1

Chapter 19 Part 1

Read more details and related context about Chapter 19 Part 1.

AA 19/20 Lecture 9

AA 19/20 Lecture 9

Maximum Margin Classifiers. Support vector machines for linear classification.

AA 19/20 Lecture 4

AA 19/20 Lecture 4

Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.

EMT Chapter 20

EMT Chapter 20

Read more details and related context about EMT Chapter 20.

AA 19/20 Lecture 6

AA 19/20 Lecture 6

Lazy learning. K-NN. Kernel regression and kernel density estimation.