Course overview
This course aims to equip students with the knowledge and skills to use the modern predictive models of deep learning, ensemble methods, and Bayesian Machine learning. Building upon concepts of predictive modelling introduced earlier, the course delves into the foundation mathematics and implementation of the methods mentioned above with a focus on how to ensure that these models precisely model the dataset considered. By implementing and interpretating the discussed models, students will develop critical thinking and problem-solving skills essential for utilizing these models to real-world problems. This course aligns with the program's intent to provide a comprehensive understanding of predictive modelling in modern data science.
Course learning outcomes
- Critically evaluate the compromises made in machine learning
- Implement and interpret deep learning models to real-world datasets
- Elucidate the foundation mathematics of deep learning, ensemble models and Bayesian machine learning
- Debate the concerns with and ethics of “black-box” modelling when implement policies