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pl:dydaktyka:ml:lab11 [2013/05/16 11:35] esimon przywrócono poprzednią wersję |
pl:dydaktyka:ml:lab11 [2019/06/27 15:50] |
====== Laboratorium 11 - Systemy rekomendacyjne i detekcja anomalii====== | |
====== Laboratorium 5 - Regresja Logistyczna ====== | |
Ćwiczenia bazujące na materiałach Andrew Ng.\\ | |
Przed zajęciami przejrzyj wykłady XV-XVI: [[https://class.coursera.org/ml/lecture/preview|Anomaly detection and recommender systems]] | |
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{{:pl:dydaktyka:ml:ex8.pdf|Instructions}} in English. | |
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Ćwiczenia do pobrania (files to download): {{:pl:dydaktyka:ml:anomaly-detection.zip|}} | |
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===== Lista i opis plików ===== | |
Pliki oznaczone znakiem wykrzyknika (:!:) należy wypełnić własnym kodem | |
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* //ex8.m// - Octave/Matlab script for first part of exercise | |
* //ex8_cofi.m// - Octave/Matlab script for second part of exercise | |
* //ex8data1.mat// - First example Dataset for anomaly detection | |
* //ex8data2.mat// - Second example Dataset for anomaly detection | |
* //ex8_movies.mat// - Movie Review Dataset | |
* //ex8_movieParams.mat// - Parameters provided for debugging | |
* //multivariateGaussian.m// - Computes the probability density function for a Gaussian distribution | |
* //visualizeFit.m// - 2D plot of a Gaussian distribution and a dataset | |
* //checkCostFunction.m// - Gradient checking for collaborative filtering | |
* //computeNumericalGradient.m// - Numerically compute gradients | |
* //fmincg.m// - Function minimization routine (similar to fminunc) | |
* //loadMovieList.m// - Loads the list of movies into a cell-array | |
* //movie_ids.txt// - List of movies | |
* //normalizeRatings.m// - Mean normalization for collaborative filtering | |
* :!: //estimateGaussian.m// - Estimate the parameters of a Gaussian distribution with a diagonal covariance matrix | |
* :!: //selectThreshold.m //- Find a threshold for anomaly detection | |
* :!: //cofiCostFunc.m// - Implement the cost function for collaborative filtering | |
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