Stochastic Processes Modelling
Markov chains, Poisson processes and Brownian motion with actuarial and financial applications.
Statistical Machine Learning & Data Analytics
Supervised and unsupervised learning methods for prediction and insight from data.
Intermediate 6 topics 12 months access Online reader
About this course
Machine learning grounded in statistics. The course covers the full workflow, from preparing data through regression, classification, tree-based ensembles and clustering, to validating models properly so they generalise to new data.
What you will learn
- Prepare and explore data for modelling
- Build regression and classification models
- Use tree-based ensembles and clustering methods
- Evaluate models with cross-validation and appropriate metrics
Course topics
- The machine learning workflow
- Regression and regularisation
- Classification methods
- Decision trees, random forests and boosting
- Clustering and dimension reduction
- Model evaluation and selection
Who this course is for
- Students in statistics, actuarial science and data science
- Analysts moving into predictive modelling
- Researchers applying machine learning to their data
Before you start
Introductory statistics; some programming experience helps.
How access works
After payment, the course materials appear in My Learning. They open in a secure online reader on your phone, tablet or computer for 12 months. Materials are view-only and cannot be downloaded. Buying again extends your access from the current end date.
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