====== Laboratorium 11 - Systemy rekomendacyjne i detekcja anomalii======

Ćwiczenia bazujące na materiałach Andrew Ng.\\
Przed zajęciami przejrzyj wykłady XV-XVI: [[https://class.coursera.org/ml/lecture/preview|Anomaly detection and recommender systems]] 

{{:pl:dydaktyka:ml:ex8.pdf|Instructions}} in English.

Ćwiczenia do pobrania (files to download): {{:pl:dydaktyka:ml:anomaly-detection.zip|}}

===== Lista i opis plików =====
Pliki oznaczone znakiem wykrzyknika (:!:) należy wypełnić własnym kodem

  * //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