Virtual University

Basic Statistical Descriptions of Data: Measuring the Central Tendency (V-U)

Basic Concepts of Classification: Introduction (V-U)

Basic Statistical Descriptions of Data: Measuring the Dispersion of Data (V-U)

Basic Concepts of Classification: Supervised vs. Unsupervised Learning (V-U)

Basic Statistical Descriptions of Data: Symmetric vs. Skewed Data (V-U)

Basic Concepts of Cluster Analysis: Challenges of Clutering (V-U)

Bayes Classification Methods: Naïve Bayes Classifier (V-U)

Basic Concepts of Cluster Analysis: Clustering Applications (V-U)

Bayes Classification Methods: Naïve Bayes Classifier: An Example (V-U)

Basic Concepts of Cluster Analysis: Clustering as a Preprocessing Tool (V-U)

Bigram based correction & its example (V-U)

Basic Concepts of Cluster Analysis: Clustering criteria (V-U)

BOAT and its advantages (V-U)

Basic Concepts of Cluster Analysis: Measure the Quality of Clustering (V-U)

Candidate generation (V-U)

Basic Concepts of Cluster Analysis: Quality: What Is Good Clustering? (V-U)

Chain rule (V-U)

Basic Concepts of Cluster Analysis: What is Cluster Analysis? (V-U)

Classification by Back propagation: A Multi-Layer Feed-Forward Neural Network (V-U)

Basic Concepts of Mining Frequent Patterns, Association and Correlation: : Closed Patterns and Max-Patterns (V-U)

Classification by Back propagation: Back propagation (V-U)

Basic Concepts of Mining Frequent Patterns, Association and Correlation: Why Is Freq. Pattern Mining Important? (V-U)

Classification by Back propagation: Defining a Network Topology (V-U)

Basic Concepts of Mining Frequent patterns: Introduction (V-U)

Classification by Back propagation: Introduction (V-U)