Machine Learning 102 Workshop at SP Jain
Unsupervised Learning – I : Cluster Analysis 1. Basic Concepts 2. Partitioning Methods 2.1. K-means 2.2. K-medoids 3. Hierarchical Methods 3.1. Diana 4. Density based Methods 4.1. DBSCAN 5. Evaluation of Clustering 6. Applications with real world problems Unsupervised Learning- II : Associations & Correlations 1. Basic Concepts 2. Frequent Itemset Mining Methods
2.2. Improving the Efficiency of Apriori
2.4. ECLAT: Frequent Pattern Mining with Vertical Data Format
3. Pattern Evaluation Methods
4. Applications with real world problems
Software and Datasets
Some software/tools and datasets needed for the training as below.