| Lecture | Subject | Material | References |
| 1 | General information about the course | slides | - |
| 2 | Introduction to data mining | slides | Textbook (1) - chapter 1, Textbook (2) - chapter 1 |
| 3 | Statistical descriptions of data, Some useful inequalities | Textbook (1) - chapter 2, Sushant Sachdeva's notes on concentration bounds pdf | |
| 4 | Finding similar items, Distance measures, minHash | Textbook (2) - chapter 3 | |
| 5 | minHash, Locality Sensitive Hashing (LSH) | Textbook (2) - chapter 3 | |
| 6 | LSH families of functions | Textbook (2) - chapter 3 | |
| 7 | Clustering, K-means algorithm | Textbook (1) - chapter 10 | |
| 8 | K-means algorithm, K-center Clustering | Textbook (1) - chapter 10, Sanjoy Dasgupta's notes pdf, Michael Dinitz's notes pdf | |
| 9 | K-Medoid clustering, Hierarchical Clustering | Textbook (1) - chapter 10 | |
| 10 | Incremental Clustering | Incremental Clustering and Dynamic Information Retrieval pdf | |
| 11 | Probabilistic Clustering, Maximum Likelihood | Ryan Martin's notes pdf. Songfeng Zheng's notes pdf | |
| 12 | Mixture Model, Expectation-Maximization | Textbook (1) - chapter 11, Alexandra Chouldechova's slides pdf | |
| 13 | Spectral Clustering | Textbook (1) - chapter 11, A tutorial on spectral clustering by Ulrike von Luxburg pdf | |
| 14 | Spectral Clustering, Continued | Textbook (1) - chapter 11, A tutorial on spectral clustering by Ulrike von Luxburg pdf | |
| 15 | Clustering Overview, Clustering Evaluation | Textbook (1) - chapter 10, Slides by Michael Hahsler pdf | |
| 16 | Outlier Detection I | Textbook (1) - chapter 12 | |
| 17 | Outlier Detection II | Textbook (1) - chapter 12 | |
| 18 | Association Rules, Frequent Itemsets, Apriori Algorithm | Textbook (1) - chapter 6 | |
| 19 | Apriori Algorithm, Data Streams, Majority Algorithm | Textbook (1) - chapter 6, paper by G. Cormode and M. Hadjieleftheriou pdf | |
| 20 | Space-Saving Algorithm, Count-Min Sketch | G. Cormode and M. Hadjieleftheriou pdf, Metwally et al pdf |