CSE012/CS059 – Data Mining

Fall 2023

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For the slides of this course we will use slides and material from other courses and books. We thank in advance:  Tan, Steinbach and Kumar, Anand Rajaraman Jeff Ullman, and Jure Leskovec, Evimaria Terzi, Aris Anagnostopoulos for the material from their slides that we have used in this course.

Introduction: Logistics (in Greek) (pptx, pdf)

Lecture 1: Introduction to Data Mining (pptx, pdf)

Lecture 2: What is data? The data mining pipeline. Preprocessing and postprocessing. Sampling and normalization. (pptx, pdf)

Lecture 3: Data exploration and statistical analysis (pptx, pdf)

  • Chapter 1 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.
  • Chapters 7-8 (confidence interval, standard error), 11 (hypothesis testing), 16 (independence and correlation tests) from the book All of Statistics byLarry A. Wasserman (the chapter numbers are for the pdf, in the actual book the chapter numbers are -1 of the ones above).
  • Error bars in experimental biology.

Lecture 4: Similarity and Distance. Recommendation Systems (pptx, pdf)

Lecture 5: Dimensionality Reduction. Singular Value Decomposition (SVD). Principal Component Analysis (PCA). Model-based collaborative filtering (pptx, pdf)

Lecture 6: Clustering. The k-means algorithm. Hierarchical Clustering. The DBSCAN algorithm. Clustering Evaluation. (pptx, pdf)

Lecture 7: Mixture Models. The EM Algorithm. (pptx, pdf)

Lecture 8: Introduction to Supervised Learning. Linear Regression. Classification. Decision Trees - Expressiveness. Evaluation. (pptx, pdf)

Lecture 9: Nearest Neighbor Classification, Support Vector Machines, Logistic Regression, (Naive Bayes Classification). Neural Networks. Word Embeddings. The Supervised Learning pipeline. (pptx, pdf)

Lecture 10: Link Analysis Ranking Web Ranking. PageRank, Random Walks, HITS. (pptx, pdf)