EE-566 Adaptation and Learning

EE-566 Adaptation and Learning

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13.2, EE-566: Lecture 13.2 1 of 15

duration 36 minutes 30 seconds

13.2, EE-566: Lecture 13.2

13.2, EE-566: Lecture 13.2

13.2, EE-566: Lecture 13.2

13.2, EE-566: Lecture 13.2

Lecture 13: Neural networks, Part 2

From Virginia Bordignon

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13.1, EE-566: Lecture 13.1 2 of 15

duration 1 hour 23 minutes

13.1, EE-566: Lecture 13.1

13.1, EE-566: Lecture 13.1

13.1, EE-566: Lecture 13.1

13.1, EE-566: Lecture 13.1

Lecture 13: Neural networks, part 1

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12.2, EE-566: Lecture 12.2 3 of 15

duration 1 hour 14 minutes

12.2, EE-566: Lecture 12.2

12.2, EE-566: Lecture 12.2

12.2, EE-566: Lecture 12.2

12.2, EE-566: Lecture 12.2

Lecture 12, Part 2: Generalization theory

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12.1, EE-566: Lecture 12.1 4 of 15

duration 1 hour 8 minutes

12.1, EE-566: Lecture 12.1

12.1, EE-566: Lecture 12.1

12.1, EE-566: Lecture 12.1

12.1, EE-566: Lecture 12.1

Lecture 12, Part 1: Kernel methods

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11.3, EE-566: Lecture 11.3 5 of 15

duration 56 minutes 1 second

11.3, EE-566: Lecture 11.3

11.3, EE-566: Lecture 11.3

11.3, EE-566: Lecture 11.3

11.3, EE-566: Lecture 11.3

Lecture 11: SVM, Part 3

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11.2, EE-566: Lecture 11.2 6 of 15

duration 48 minutes 13 seconds

11.2, EE-566: Lecture 11.2

11.2, EE-566: Lecture 11.2

11.2, EE-566: Lecture 11.2

11.2, EE-566: Lecture 11.2

Lecture 11: Perceptron, Part 2

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11.1, EE-566: Lecture 11.1 7 of 15

duration 40 minutes 12 seconds

11.1, EE-566: Lecture 11.1

11.1, EE-566: Lecture 11.1

11.1, EE-566: Lecture 11.1

11.1, EE-566: Lecture 11.1

Lecture 11: Logistic Regression, Part 1

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10.3, EE-566: Lecture 10.3 8 of 15

duration 45 minutes 15 seconds

10.3, EE-566: Lecture 10.3

10.3, EE-566: Lecture 10.3

10.3, EE-566: Lecture 10.3

10.3, EE-566: Lecture 10.3

Lecture 10: PCA

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10.2, EE-566: Lecture 10.2 9 of 15

duration 57 minutes 7 seconds

10.2, EE-566: Lecture 10.2

10.2, EE-566: Lecture 10.2

10.2, EE-566: Lecture 10.2

10.2, EE-566: Lecture 10.2

Lecture 10: Linear discriminant analysis

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10.1, EE-566: Lecture 10.1 10 of 15

duration 53 minutes 37 seconds

10.1, EE-566: Lecture 10.1

10.1, EE-566: Lecture 10.1

10.1, EE-566: Lecture 10.1

10.1, EE-566: Lecture 10.1

Lecture 10: Naive Bayes classifier

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9.2, EE-566: Lecture 9.2 11 of 15

duration 1 hour 13 minutes

9.2, EE-566: Lecture 9.2

9.2, EE-566: Lecture 9.2

9.2, EE-566: Lecture 9.2

9.2, EE-566: Lecture 9.2

Lecture 9: Nearest neighbor rules, Part 2

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9.1, EE-566: Lecture 9.1 12 of 15

duration 53 minutes 27 seconds

9.1, EE-566: Lecture 9.1

9.1, EE-566: Lecture 9.1

9.1, EE-566: Lecture 9.1

9.1, EE-566: Lecture 9.1

Lecture 9: L1 Regularization, Part 1

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8.2, EE-566: Lecture 8.2 13 of 15

duration 44 minutes 37 seconds

8.2, EE-566: Lecture 8.2

8.2, EE-566: Lecture 8.2

8.2, EE-566: Lecture 8.2

8.2, EE-566: Lecture 8.2

Lecture 8.2: Regularization

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8.1, EE-566: Lecture 8.1 14 of 15

duration 1 hour 36 minutes

8.1, EE-566: Lecture 8.1

8.1, EE-566: Lecture 8.1

8.1, EE-566: Lecture 8.1

8.1, EE-566: Lecture 8.1

Lecture 8: Least Squares

From Virginia Bordignon

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7.2, EE-566: Lecture 7.2 15 of 15

duration 1 hour 0 minutes

7.2, EE-566: Lecture 7.2

7.2, EE-566: Lecture 7.2

7.2, EE-566: Lecture 7.2

7.2, EE-566: Lecture 7.2

Lecture 7: Maximum Likelihood

From Virginia Bordignon

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