Part II: Machine Learning

Note: L2D's Basic Python & Data Handling course (or its equivalent) is a compulsory prerequisite to taking the Machine Learning component of L2D. Supervised Machine Learning: Key areas taught in L2D's exploration of Supervised Machine Learning include:

• Classification: preparing data for classification, training classifier models.
• State space plot of model predictions
• Prediction probabilities and feature importance
• Complex training and testing of data
• Comparison of different model classes
• Stratified shuffle split
• Evaluation of classification using AUC and ROC curves
• Metrics for model evaluation
• Permutation scoring and confusion matrices
• Normalisation and hyperparameter tuning
• Refinement and progressive adjustment

Unsupervised Machine Learning: Key areas taught in L2D's exploration of Unsupervised Machine Learning include:

• Gaussian Mixture Models (GMMs) and sci-kit learn
• Clustering and automated data labelling
• Quantitative scoring using ground truth
• Introductions to the concept and pitfalls of clustering techniques
• GMMs in medical image segmentation and object detection
• Dimensionality reduction (reducing computational workloads of large high-dimensionality datasets)
• PCA (Principal Component Analysis)

Note: L2D's Basic Python and Data Handling modules (or their equivalent) are compulsory requirements to taking our Machine Learning course.