├── README.md ├── notebooks ├── 1a_eigenvalues.ipynb ├── 1b_svd.ipynb ├── 1c_pca.ipynb ├── 2a_FPGrowth_python.ipynb ├── 3a_hits_networkx.ipynb ├── 3b_pagerank.ipynb ├── 3c_pagerank_mapreduce.ipynb ├── 4c_modelado_factor_latente.ipynb ├── 4d_casoNetflix.ipynb ├── 4e_learningToRank.ipynb ├── 5a_algoritmo_Girvan_Newman.ipynb ├── 5b_SimRank.ipynb ├── 6b_spectral_clustering.ipynb ├── Books.csv ├── FPGrowth_spark.ipynb ├── UserEvents.csv ├── Users.csv └── data_hist.txt └── slides ├── 0Presentación_curso.pdf ├── 1a_Vectores_valores_propios.pdf ├── 1b_IntroSVD.pdf ├── 1c_CUR.pdf ├── 1d_PCA.pdf ├── 1e_Proyecciones_aleatorias.pdf ├── 2a_modelo_mercado_canasta.pdf ├── 2b_AlgoritmoApriori.pdf ├── 2c_AlgoritmosMemoriaPrincipal.pdf ├── 2d_Algoritmos_PasadasLimitadas.pdf ├── 2e_arbolesEnumeración.pdf ├── 3a_HITS.pdf ├── 3b_pageRank.pdf ├── 3c_PageRank_sensibleTopico.pdf ├── 3d_trustRank.pdf ├── 4a_Introducción.pdf ├── 4b_contenido.pdf ├── 4c_Filtrado.pdf ├── 4d_CasoNetflix.pdf ├── 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