Note: This iteration of the course is currently not open for booking. However, please register your interest here to be notified when spaces become available. Your registration ensures you will be the first to know.

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.

If you do not have a University of Cambridge Raven account please book or register your interest here.
If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry.

Additional information

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Research Informatics Training Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.