Chapter Fifteen Anomaly Detection-Machine Learning Teacher's Chalkboard-Professor Stanford Wu Enda

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Chapter 15 Anomaly Detection

15.1 Problem motivation
15.2 Gaussian distribution
15.3 Algorithm
15.4 Improve and evaluate an anomaly detection system
15.5 Anomaly detection vs supervised learning
15.6 Select the features to use
15.7 Multivariate Gaussian distribution
15.8 Anomaly detection using multivariate Gaussian distribution

15.1 Problem motivation

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15.2 Gaussian distribution

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15.3 Algorithm

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15.4 Improve and evaluate an anomaly detection system

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15.5 Anomaly detection vs supervised learning

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15.6 Select the features to use

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15.7 Multivariate Gaussian distribution

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15.8 Anomaly detection using multivariate Gaussian distribution

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Chapter Fifteen Anomaly Detection-Machine Learning Teacher's Chalkboard-Professor Stanford Wu Enda

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Chapter 10 Recommendations for Using Machine Learning-Machine Learning Teacher's Chalkboard-Professor Stanford Wu Enda

Chapter 4 Multivariate Linear Regression-Machine Learning Teacher's Book-Professor Stanford Wu Enda

Chapter VII Regularization-Machine Learning Teacher's Writing-Professor Stanford Wu Enda

Chapter 6 Logistic Regression-Machine Learning Teacher's Writing Board-Professor Stanford Wu Enda

Chapter 1 Introduction-Machine Learning Teacher's Writing-Professor Stanford Wu Enda

Chapter Sixteen Recommendation System-Machine Learning Teacher's Writing Board-Professor Stanford Wu Enda

Chapter 9 Neural Network Learning-Machine Learning Teacher's Writing-Professor Stanford Professor Wu Enda

Chapter Eleven Machine Learning System Design-Machine Learning Teacher Writing-Professor Stanford Wu Enda

Chapter 2 Univariate Linear Regression-Machine Learning Teacher's Writing Board-Professor Stanford Wu Enda

Chapter Five Octave Tutorial-Machine Learning Teacher Chalkboard-Professor Stanford Wu Enda

Chapter Treize-Machine Learning Teacher Chalkboard-Professor Stanford Wu Enda

Chapitre 10 Recommandations pour l'utilisation du Machine Learning-Machine Learning Teacher Chalkboard-Professor Stanford Wu Enda

1.2 Orthogonalization-The third lesson of deep learning "Structured Machine Learning Project"-Professor Stanford Wu Enda

0.0 Table of Contents-The Third Lesson of Deep Learning "Structured Machine Learning Project"-Professor Stanford Wu Enda

Coursera Stanford Wu Enda Machine Learning Course Notes (2)

1.5 Training / Development / Testing Partition-Deep Learning Lesson 3 "Structured Machine Learning Project" -Professor Stanford Wu Enda

1.1 Why ML strategy is needed-Deep Learning Lesson 3 "Structured Machine Learning Project"-Professor Stanford Wu Enda

1.4 Satisfying and optimizing indicators-Deep Learning Lesson 3 "Structured Machine Learning Project"-Professor Stanford Wu Enda

1.6 The size of the development set and test set-Deep Learning Lesson 3 "Structured Machine Learning Project"-Professor Stanford Wu Enda

1.10 Understanding Human Performance-The Third Course of Deep Learning "Structured Machine Learning Project"-Professor Stanford Wu Enda

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