CSE012/CS059 – Data Mining

Fall 2015

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.

Lecture 2: Probability Theory. What is data? (pptx, pdf)

Intoduction to probability Theory (thanks to Aris Anagnostopoulos and Eli Upfal)
Chapter 2 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.

Lecture 3: The data mining pipeline. Preprocessing and postprocessing. Samping and normalization (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.

Tutorial 1: Introduction to Python programming language. (pptx, pdf, ipynb)

Lecture 4: The data mining pipeline. Preprocessing and postprocessing. Samping and normalization (pptx, pdf)

Chapter 6 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Chapter 6 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.

Tutorial 2: Introduction to Pandas library. (pptx, pdf, ipynb)

Lecture 5: 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 6: Finding similar pairs. Min-hash signatures. Locality Sensitive Hashing (pptx, pdf)

Chapter 3 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.

Lecture 7: 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.

Lecture 8: Clustering. The k-means algorithm. Hierarchical Clustering. The DBSCAN algorithm (pptx, pdf)

Chapter 8 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.

Tutorial 3: Introduction to Numpy, Scipy, SciKit for handling matrix operations. (ipynb)

Lecture 9: Mixture Models. The EM Algorithm. Evaluation (pptx, pdf)

Chapters 8,9 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Notes on the EM algorithm by Aris Anagnostopoulos, University of Rome La Sapienza

Lecture 10: Classification. Decision Trees. Evaluation. (pptx, pdf)

Chapter 4 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.

Tutorial 4: Introduction to Clustering and Text Processing with SciKit. (ipynb)

Lecture 11: Other classification techniques. Nearest Neighbor Classifiers, Support Vector Machines, Logistic Regression, Naive Bayes Classification. Supervised Learning. (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

Tutorial 5: Introduction to Classification with SciKit. (ipynb)

Lecture 12: Link Analysis Ranking Web Ranking. PageRank, Random Walks, HITS. (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

Tutorial 6: Introduction to Network analysis with NetworkX. (ipynb)

Lecture 13: Absorbing Random Walks. Coverage Problems. (pptx, pdf)

P. G. Doyle and J. L. Snell. Absorbing Random Walks and Electrical Networks.
Chapter 2 from the book "Approximation Algorithms" of V. Vazirani