Course overview
This course aims to equip learners with the knowledge and skills to use the modern predictive machine learning models, including kernel methods, Bayesian machine learning and deep learning. Building upon concepts of predictive modelling introduced earlier, the course delves into the foundation mathematics and properties of the methods mentioned above with a focus on how to ensure that these models precisely model the dataset considered. By studying the properties and interpretation of the discussed models, learners 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.
- Foundations of Statistical Machine Learning
- Kernel and Bayesian Methods
- Deep Learning and Emerging Topics
Course learning outcomes
- Critically evaluate the compromises made in machine learning
- Describe and interpret the application of 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