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Data Mining (Spring 2023) - Statistical Principles
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Data Mining - Spring 2023
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- 1 Data Mining (Spring 2023) - Class Overview
- 2 Data Mining (Spring 2023) - Statistical Principles
- 3 Data Mining (Spring 2023) – k-grams and Jaccard similarity
- 4 Data Mining (Spring 2023) - Min Hashing
- 5 Data Mining (Spring 2023) - Finishing Min Hashing
- 6 Data Mining (Spring 2023) - Distances
- 7 Data Mining (Spring 23) - Embeddings/Representations
- 8 Data Mining (Spring 2023) - Hierarchical Clustering
- 9 Data Mining (Spring 2023) - k-means
- 10 Data Mining (Spring 2023) - Spectral Clustering
- 11 Data Mining (Spring 2023) - Streaming (Misra–Gries)
- 12 Data Mining (Spring 2023) - Sketch Algorithms
- 13 Data Mining (Spring 2023) - Count Sketch
- 14 Data Mining (Spring 2023) - Linear Regression
- 15 Data Mining (Spring 2023) - Nonlinear regression & Regularization
- 16 Data Mining (Spring 2023) - PCA & SVD
- 17 Data Mining (Spring 2023) - Random Projection
- 18 Data Mining (Spring 2023) - Frequent Directions
- 19 Data Mining (Spring 2023) - Multidimensional scaling (MDS) & Linear discriminant analysis (LDA)
- 20 Data Mining (Spring 2023) - Distance metric learning + Outlier detection
- 21 Data Mining (Spring 2023) - Matrix completion; PageRank
- 22 Data Mining (Spring 2023) - PageRank Part II
- 23 Data Mining (Spring 2023) - MapReduce
- 24 Data Mining (Spring 2023) - Review
