Unit I
Mathematical concepts review – Central tendency – Dispersion of data – Descriptive data summaries – k-nearest neighbor classifier – Bayes classifiers – Classifier performance measures
Unit II
Decision tree – Ensemble methods – Ordinary Least Squares – Artificial neurons – Perceptron – Multi Layer Perceptron and backpropagation -Hyperparameter tuning – Cluster analysis – Partitioning methods – Hierarchical methods -Density-based methods – Cluster evaluation Unit III
Graphs – Definitions and applications – Graph Connectivity – Graph Traversal – Testing Bipartiteness – Breadth-First Search – Directed graphs – Directed Acyclic Graphs -Topological
ordering – Interval scheduling – Optimal caching – shortest paths – Minimum Spanning Tree – Clustering – Huffman Codes – Data Compression – Partitioning Problems – Graph Coloring