CSE012/CS059 – Data Mining

Fall 2020

Lecture Slides

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 of their slides that we have used in this course.

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

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

Chapters 1,2 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Article: Data Scientist: The Sexiest Job of the 21st Century.

Tutorial 1: Introduction to discrete probabilities. (pdf)

Thanks to Aris Anagnostopoulos for the slides.
Part I from the book All of Statistics by Larry A. Wasserman

Lectures 2-3: What is data? The data mining pipeline. Preprocessing and postprocessing. Sampling and normalization. Data exploration and statistical analysis (pptx, pdf)

Chapters 2,3 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
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).

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

Chapter 2 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Chapters 3,9 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.
Article: The long tail.

Lecture 5: Dimensionality Reduction. Singular Value Decomposition (SVD). Principal Component Analysis (PCA). (pptx, pdf)

Chapter 11 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.
Appendices A,B from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
A tutorial on Principal Component Analysis, Jonathon Shlens

Tutorial 2: Introduction to notebooks and the Pandas library

Slides: pptx, pdf
Notebook with Python Reminders (ipynb, html, html slides, pdf)
Notebook for Pandas (ipynb, html, html slides, pdf)
Notebook for Plotting, Statistical Significance (ipynb, html, html slides, pdf)
The files for the notebooks
Notes from the class of Evimaria Terzi and Mark Crovella

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

Chapters 8,9 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Chapter 7 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.

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

Notes on the EM algorithm by Aris Anagnostopoulos, University of Rome La Sapienza

Tutorial 3: Introduction to the Numpy library (Notebook: ipynb, html, html slides, pdf).

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

Chapter 14 (in the pdf) from the book All of Statistics byLarry A. Wasserman
Chapter 4 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.

Tutorial 4: Introduction to the SciKit-Learn library and its applications to clustering and data processing (Notebook: ipynb, html, html slides, pdf).

Lecture 9: Other classification techniques. Nearest Neighbor Classifiers, Support Vector Machines, Logistic Regression, Naive Bayes Classification. The Supervised Learning pipeline. (pptx, pdf)

Chapters 4,5 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Chapter 12 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.
Chapter 13 from the book "Introduction to Information Retrieval" by C. Manning, P. Raghavan, H. Schutze
Chris Manning, Natural Language Processing with Deep Learning, Lecture Notes, Part I

Tutorial 5: Introduction to the scikir-learn library and applciations for classification and data processing (Notebook: ipynb, html, html slides).

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

Chapter 21 from the book "Introduction to Information Retrieval" by C. Manning, P. Raghavan, H. Schutze
Chapter 14 from the book "Networks Crowds and Markets" of D. Easley and J. Kleinberg
P. G. Doyle and J. L. Snell. Absorbing Random Walks and Electrical Networks.
Grindstead and Snell's Introduction to Probability (Chapter 11)

Tutorial 6: Introduction to the library NetworkX (Notebook: ipynb, html, html slides, pdf).

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