ML & AI: Machine Learning for Researchers
Machine learning is less foreign than it sounds once you've done regression. A neural network is stacked logistic regressions; a random forest is a committee of if-then rules. This course is built for the researcher who knows the Statistics Core and wants the ML vocabulary without the hype or the heavy math.
The hinge is one shift in goal. Classical statistics asks why; machine learning asks how well can we predict the next case, and that single change reorganizes everything, starting with why you must hold out a test set and never grade a model on data it has seen. From there: regularization when predictors outnumber sense, the accuracy trap and ROC curves, the tree-based workhorses (trees, forests), clustering and dimensionality reduction when there are no labels, and a grounded look at neural networks and large language models in a research workflow.
Come with Stats 3-level regression; the lessons keep pointing back to it. This is where the two tracks meet.