ETC3250/5250 Introduction to Machine Learning
Home
Schedule
Week 1: Foundations of machine learning
Week 2: Visualising your data and models
Week 3: Re-sampling and regularisation
Week 4: Logistic regression and discriminant analysis
Week 5: Trees and forests
Week 6: Neural networks and deep learning
Week 7: Explainable artificial intelligence (XAI)
Week 8: Support vector machines and nearest neighbours
Week 9: K-means and hierarchical clustering
Week 10: Model-based clustering and self-organising maps
Week 11: Evaluating your clustering model
Week 12: Project presentations by Masters students
Discussion
Moodle
Resources
On this page
Main reference
Presentations from Masters students
Tutorial instructions
Week 12: Project presentations by Masters students
Main reference
Presentations from Masters students
The goal here is to learn interesting aspects of the data and model.
Tutorial instructions
Instructions:
html
qmd