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pl:dydaktyka:ml:lab11 [2017/07/17 10:08] |
pl:dydaktyka:ml:lab11 [2019/06/27 15:50] (aktualna) |
| ====== Laboratorium 11 - Systemy rekomendacyjne i detekcja anomalii====== |
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| 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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