This course on unsupervised learning provides a systematic introduction to dimensionality reduction and clustering techniques. The course covers fundamental concepts of unsupervised learning and data normalization, then progresses through the practical applications of Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and hierarchical clustering algorithms.

The course emphasizes both theoretical understanding and hands-on application, teaching students to recognize when different techniques are appropriate and when they may fail. A key learning objective is understanding the limitations of linear methods like PCA. Students learn to evaluate the performance of unsupervised learning methods across diverse data types, with the ultimate goal of generating meaningful hypotheses for further research.

https://training.cam.ac.uk/event/6091740