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

2.1. Apriori

2.2. Improving the Efficiency of Apriori

2.3. FPGrowth

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.

This is a training on Machine Learning with R for the Big Data and Analytics course, the S P Jain School of Global Management, Mumbai, India, 30 May - 5 June 2016.

Contents of Training