lectures

Discretization using Value Reduction, Weka Demo || ||   || Receiver Operating Characteristics (ROC Curve), Lift and Gain Charts, KNIME Demo, Lab Session: Weka and KNIME || ||   || K-Means: Concepts, Working, Limitations, Schemes to Handle Initial Centroid Problems in K-Means || ||   ||
 * ** S.# ** || ** Date ** || ** Day ** || ** Topic ** || ** Download ** || ** Comments ** ||
 * 1 || 4/9/2013 || Wednesday || Course Overview, What is Data Mining and its Origin, Typical Data Mining Tasks, Data Mining Applications/Examples || [[file:Data Mining Unit 1.pdf]] ||  ||
 * || 11/9/2013 || Wednesday || Class cancelled - due to law & order situation in the city ||  ||   ||
 * 2-3 || 18/9/2013 || Wednesday || Data Mining vs. OLAP and Statistics, Data Preparation, Normalization, Outlier Detection, Feature Reduction/Ranking using Mean-Variance Method, Sampling Size, Introduction to Classification/Decision Trees, Model Interpretation, Measures of Node Impurity, Computation of GINI Index || [[file:Data Mining Unit 2.pdf]] ||  ||
 * 4-5 || 23/9/2013 || Monday || Computation of Entropy and Misclassification Error, Induction of Classification Trees,Handling of Continuous and Multi-state Data, ChiMerge Discretization,
 * 6-7 || 25/9/2013 || Wednesday || Accuracy, Weighted Accuracy, Recall and Precision,
 * 8-9 || 2/10/2013 || Wednesday || Bayes Theorem, Naive Bayes Classifier, KNIME/Weka Discussion, Revision before Midterm 1 || [[file:Data Mining Unit 5.pdf]] ||  ||
 * 10-11 || 23/10/2013 || Wednesday || Entropy-based Feature Selection, Artificial Neural Networks, Motivation, History, Multi-layer Feedforward Network, Backpropagation Algorithm || [[file:Data Mining Unit 6.pdf]] ||  ||
 * 12-13 || 30/10/2013 || Wednesday || Lazy Learner vs. Eager Learner, k-Nearest Neighbor: Pros and Cons, HW 1 Presentations || [[file:Data Mining Unit 7.pdf]] ||  ||
 * 14-15 || 6/11/2013 || Wednesday || Model Evaluation (Holdout, k-Cross Validation), Sampling with Replacement (Bootsrapping), Ensemble Methods (Bagging and Boosting), Stacking, Clustering: Basic Concepts and Popular Types, Applications,
 * 16-17 || 18/11/2013 || Monday || Hierarchical Clustering: Simple/Complete/Average Linkages, Validity of Clusters: External and Internal Metrics, Distance Computation for Mixed Type Variables: Interval-Scaled, Symmetric and Asymmetric Binary, Categorical and Ordinal || [[file:Data Mining Unit 9.pdf]] ||  ||
 * 18 || 20/11/2013 || Wednesday || Project 1 Presentation ||  ||   ||
 * 19-20 || 11/12/2013 || Wednesday || KNIME Case Studies Presentations ||  ||   ||
 * 21-22 || 15/12/2013 || Sunday || Covariance Matrix, Eigenvalues and Eigenvectors, Principal Component Analysis, R and KNIME implementation of PCA, Guest Lecture by Mr. Nauman Sheikh || [[file:Data Mining Unit 10.pdf]] ||  ||
 * 23-24 || 23/12/2013 || Monday || Unit # 10 (Cont'd), Association Rule Mining, Association Rule Mining, Apriori Algorithm, Frequent Itemsets and Rules Generation, Support, Confidence, Interest and Lift, Introduction to R-Programming || [[file:Data Mining Unit 11.pdf]] ||  ||
 * 25-26 || 30/12/2013 || Monday || Clustering (Fuzzy c-Mean, SOM, ART), Big Data ||  ||   ||
 * 27-28 || 1/12/2013 || Wednesday || Project II Presentations ||  ||   ||