Efficient hyperparameter tuning using Bayesian optimization

This thesis addresses many open challenges in hyperparameter tuning of machine learning algorithms. The thesis develops efficient Bayesian optimization frameworks for hyperparameter tuning by utilizing different techniques like transfer learning, parallel computing, and domain-specific prior knowledge induction.