* class lectures
  * {{:lecture_1b.pdf||Lecture 1 - Introduction to Data Mining}}    
  * {{:lecture_2.pdf||Lecture 2 - Continue with Introduction, data mining concepts}} 
  * {{:civddd_oct19_006.pdf|Lecture 2 example- Visualization}} 
  * {{:03preprocessing.pdf|Lecture 3 - Introduction to Preprocessing}} 
  * {{:lecture_4.pdf|Lecture 4 - Introduction to Data Warehouses and Data Cubes, DBLEARN}}   
  * {{:lecture_5.pdf|Lecture 5 - Introduction to Rough Sets}}    
  * {{:approximating_answers_20140923_-_no_notes.pdf|Lecture 5+ - Approximating Answers - Guest Graham Toppin}}    
  * {{:lecture_6_assorule1.pdf|Lecture 6 - Association Rule Mining - Guest Aijun An}}    
  * {{:lecture_7.pdf|Lecture 7 - Classification & Prediction}}    
  * {{:lecture_7a.pdf|Lecture 7a - Introduction to ELEM2}}    
  * {{:6-slides.pdf|Lecture 7b - 6 slides}}    
  * {{:lecture_8.pdf|Lecture 8 - Introduction to Neural Networks}}    
  * {{:lecture_8a.pdf|Lecture 8a - More on Neural Networks}}    
  * {{:neural13.pdf|Lecture 8b - Andrew Moore on Neural Networks}}    
  * {{:weka.pdf|Lecture 9 - Introduction to WEKA}}    
  * {{:lecture_10.pdf|Lecture 10 - Clustering}}    
  * {{:k_nearest_neighbor_algorithm.pdf|Lecture 11 - K NN}}    
  * {{:maisha_fariha_rule_based_classification.pdf|Lecture 12 - Rule-Based Classification (Maisha Fariha)}}    
  * {{:l13.pdf|Lecture 13 - Support Vector Machines from you tube - Andrew Ng & Pat Winston}}\\   
  * {{:prediction_and_classification_with_k-nearest_neighbors.pdf|Lecture 14 - Prediction and Classification with k-Nearest Neighbors (Yuping Lin)}}\\    
  * {{:bonryu_clusteranalysis.pdf|Lecture 15 - Clustering, EM algorithm (Bon Ryu)}}\\    
  * {{:lecture_14c.pdf|Lecture 15a - k-Means Clustering, Outliers and anomaly detection, EM algorithm}}\\    
  * {{:lecture_16b.pdf|Lecture 16 - Recommendation Systems: Collaborative Filtering}}\\    
  * {{:lecture_16othera.pdf|Lecture 16 other - Recommendation Systems videos}}\\    
  * {{:slides.pdf|Lecture 17 Graph Mining, Social Network Analysis (Vincent Chu & Darren Rolfe)}}\\    
  * {{:mapforum.pdf|Lecture 18a Spatial/Spatio-temporal Data Mining}}\\    
  * {{:may-tutorial-sebd-07.pdf|Lecture 18b Tutorial on Geographical and Spatial Data Mining}}\\    
  * {{:csdeptfall10.pdf|Lecture 18c Geoinformatics across disciplines}}\\    
  * {{:sdm_slide_0903.pdf|Lecture 18d What's so Special about Spatial Data Mining}}\\    
  * {{:text-cat-tutorial.pdf|Lecture 19 Text Classification (William Cohen)}}\\    
  * {{:{{:lecture_18_ngram_models.pdf|textclassify.pdf}}|Lecture 19a Text Classification (Aijun An)}}\\    
  * {{:lec19.pdf|Lecture 19b Text Classification short videos}}\\    
  * {{:text_mining.pdf|Lecture 20 Text Mining, Mining the World Wide Web (Van Le)}}\\    
  * {{:ensemble.pdf|Lecture 21 Bagging, Boosting and Stacking (Mingbin Xu)}}\\    
  * {{:lecture_18_ngram_models.pdf|Lecture 22 n-gram (Bahareh Sarrafzadeh)}}\\    
  * {{:ngram.pdf|Lecture 22 (Vlado Keselj)}}\\    
  * {{:pacling05.pdf|ngrams}}\\    
  * {{:last_class_2.pdf|data mining videos}}\\    
  * {{:the_tugboat.pdf|The Tugboat}}\\    
  * {{:driving_in_bolivia.pdf|Driving in Bolivia}}\\    


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  * {{:pwheel-vldb.pdf|Potter's wheel - Raman et al.}}    
  * {{:cluster_analysis.pdf|Cluster analysis example}}